Overview

Dataset statistics

Number of variables69
Number of observations1000
Missing cells1194
Missing cells (%)1.7%
Total size in memory511.8 KiB
Average record size in memory524.1 B

Variable types

Numeric35
Text24
DateTime5
Boolean4
Unsupported1

Alerts

has_availability is highly imbalanced (75.8%)Imbalance
calendar_updated has 1000 (100.0%) missing valuesMissing
first_review has 97 (9.7%) missing valuesMissing
last_review has 97 (9.7%) missing valuesMissing
host_id is highly skewed (γ1 = 24.01751487)Skewed
id has unique valuesUnique
listing_url has unique valuesUnique
picture_url has unique valuesUnique
calendar_updated is an unsupported type, check if it needs cleaning or further analysisUnsupported
bedrooms has 260 (26.0%) zerosZeros
availability_30 has 227 (22.7%) zerosZeros
availability_60 has 185 (18.5%) zerosZeros
availability_90 has 164 (16.4%) zerosZeros
availability_365 has 97 (9.7%) zerosZeros
number_of_reviews has 97 (9.7%) zerosZeros
number_of_reviews_ltm has 349 (34.9%) zerosZeros
number_of_reviews_l30d has 706 (70.6%) zerosZeros
calculated_host_listings_count_entire_homes has 169 (16.9%) zerosZeros
calculated_host_listings_count_private_rooms has 688 (68.8%) zerosZeros

Reproduction

Analysis started2023-10-24 00:02:35.620607
Analysis finished2023-10-24 00:02:39.271818
Duration3.65 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1103831.366
Minimum17878
Maximum2836607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:02:39.782697image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum17878
5-th percentile141229.45
Q1522593.5
median943605
Q31763560
95-th percentile2105885.05
Maximum2836607
Range2818729
Interquartile range (IQR)1240966.5

Descriptive statistics

Standard deviation664897.6279
Coefficient of variation (CV)0.6023543526
Kurtosis-1.224874276
Mean1103831.366
Median Absolute Deviation (MAD)586553.5
Skewness0.1939529174
Sum1103831366
Variance4.420888556 × 1011
MonotonicityNot monotonic
2023-10-23T21:02:40.602986image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
231497 1
 
0.1%
1487694 1
 
0.1%
1466924 1
 
0.1%
1471275 1
 
0.1%
1223867 1
 
0.1%
1224109 1
 
0.1%
1474977 1
 
0.1%
1479860 1
 
0.1%
1226009 1
 
0.1%
1483182 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
17878 1
0.1%
25026 1
0.1%
35764 1
0.1%
48305 1
0.1%
48901 1
0.1%
ValueCountFrequency (%)
2836607 1
0.1%
2283286 1
0.1%
2283279 1
0.1%
2283224 1
0.1%
2282916 1
0.1%

listing_url
Text

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:02:41.465264image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length36
Median length35
Mean length35.443
Min length34

Characters and Unicode

Total characters35443
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st rowhttps://www.airbnb.com/rooms/231497
2nd rowhttps://www.airbnb.com/rooms/231516
3rd rowhttps://www.airbnb.com/rooms/236991
4th rowhttps://www.airbnb.com/rooms/17878
5th rowhttps://www.airbnb.com/rooms/900709
ValueCountFrequency (%)
https://www.airbnb.com/rooms/231497 1
 
0.1%
https://www.airbnb.com/rooms/247779 1
 
0.1%
https://www.airbnb.com/rooms/70547 1
 
0.1%
https://www.airbnb.com/rooms/262466 1
 
0.1%
https://www.airbnb.com/rooms/236991 1
 
0.1%
https://www.airbnb.com/rooms/17878 1
 
0.1%
https://www.airbnb.com/rooms/900709 1
 
0.1%
https://www.airbnb.com/rooms/25026 1
 
0.1%
https://www.airbnb.com/rooms/238802 1
 
0.1%
https://www.airbnb.com/rooms/239531 1
 
0.1%
Other values (990) 990
99.0%
2023-10-23T21:02:43.002172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 4000
 
11.3%
o 3000
 
8.5%
w 3000
 
8.5%
b 2000
 
5.6%
m 2000
 
5.6%
s 2000
 
5.6%
. 2000
 
5.6%
t 2000
 
5.6%
r 2000
 
5.6%
h 1000
 
2.8%
Other values (16) 12443
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22000
62.1%
Other Punctuation 7000
 
19.8%
Decimal Number 6443
 
18.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3000
13.6%
w 3000
13.6%
b 2000
9.1%
m 2000
9.1%
s 2000
9.1%
t 2000
9.1%
r 2000
9.1%
h 1000
 
4.5%
n 1000
 
4.5%
c 1000
 
4.5%
Other values (3) 3000
13.6%
Decimal Number
ValueCountFrequency (%)
1 954
14.8%
2 759
11.8%
7 663
10.3%
0 645
10.0%
8 635
9.9%
9 600
9.3%
5 572
8.9%
6 566
8.8%
3 536
8.3%
4 513
8.0%
Other Punctuation
ValueCountFrequency (%)
/ 4000
57.1%
. 2000
28.6%
: 1000
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 22000
62.1%
Common 13443
37.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 4000
29.8%
. 2000
14.9%
: 1000
 
7.4%
1 954
 
7.1%
2 759
 
5.6%
7 663
 
4.9%
0 645
 
4.8%
8 635
 
4.7%
9 600
 
4.5%
5 572
 
4.3%
Other values (3) 1615
12.0%
Latin
ValueCountFrequency (%)
o 3000
13.6%
w 3000
13.6%
b 2000
9.1%
m 2000
9.1%
s 2000
9.1%
t 2000
9.1%
r 2000
9.1%
h 1000
 
4.5%
n 1000
 
4.5%
c 1000
 
4.5%
Other values (3) 3000
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35443
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 4000
 
11.3%
o 3000
 
8.5%
w 3000
 
8.5%
b 2000
 
5.6%
m 2000
 
5.6%
s 2000
 
5.6%
. 2000
 
5.6%
t 2000
 
5.6%
r 2000
 
5.6%
h 1000
 
2.8%
Other values (16) 12443
35.1%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2023-09-22 00:00:00
Maximum2023-09-23 00:00:00
2023-10-23T21:02:43.935710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-23T21:02:45.198406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

source
Text

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:02:45.640946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length15
Median length11
Mean length11.384
Min length11

Characters and Unicode

Total characters11384
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcity scrape
2nd rowcity scrape
3rd rowcity scrape
4th rowcity scrape
5th rowcity scrape
ValueCountFrequency (%)
scrape 1000
50.0%
city 904
45.2%
previous 96
 
4.8%
2023-10-23T21:02:46.833657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 1904
16.7%
s 1096
9.6%
r 1096
9.6%
p 1096
9.6%
e 1096
9.6%
i 1000
8.8%
1000
8.8%
a 1000
8.8%
t 904
7.9%
y 904
7.9%
Other values (3) 288
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10384
91.2%
Space Separator 1000
 
8.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 1904
18.3%
s 1096
10.6%
r 1096
10.6%
p 1096
10.6%
e 1096
10.6%
i 1000
9.6%
a 1000
9.6%
t 904
8.7%
y 904
8.7%
v 96
 
0.9%
Other values (2) 192
 
1.8%
Space Separator
ValueCountFrequency (%)
1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10384
91.2%
Common 1000
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 1904
18.3%
s 1096
10.6%
r 1096
10.6%
p 1096
10.6%
e 1096
10.6%
i 1000
9.6%
a 1000
9.6%
t 904
8.7%
y 904
8.7%
v 96
 
0.9%
Other values (2) 192
 
1.8%
Common
ValueCountFrequency (%)
1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 1904
16.7%
s 1096
9.6%
r 1096
9.6%
p 1096
9.6%
e 1096
9.6%
i 1000
8.8%
1000
8.8%
a 1000
8.8%
t 904
7.9%
y 904
7.9%
Other values (3) 288
 
2.5%

name
Text

Distinct802
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:02:47.649600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length83
Median length75
Mean length64.343
Min length36

Characters and Unicode

Total characters64343
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique699 ?
Unique (%)69.9%

Sample

1st rowRental unit in Rio de Janeiro · ★4.73 · 1 bedroom · 1 bed · 1 bath
2nd rowRental unit in Rio de Janeiro · ★4.71 · 1 bedroom · 1 bed
3rd rowRental unit in Rio de Janeiro · ★4.89 · 1 bedroom · 4 beds · 1 bath
4th rowCondo in Rio de Janeiro · ★4.70 · 2 bedrooms · 2 beds · 1 bath
5th rowRental unit in Rio de Janeiro · 1 bedroom · 1 bed · 1 bath
ValueCountFrequency (%)
· 3809
23.6%
1 1433
 
8.9%
in 1000
 
6.2%
rio 947
 
5.9%
de 819
 
5.1%
janeiro 819
 
5.1%
rental 778
 
4.8%
unit 778
 
4.8%
2 704
 
4.4%
beds 664
 
4.1%
Other values (160) 4393
27.2%
2023-10-23T21:02:49.233853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15149
23.5%
e 4701
 
7.3%
o 4009
 
6.2%
· 3809
 
5.9%
i 3669
 
5.7%
n 3508
 
5.5%
d 2982
 
4.6%
b 2956
 
4.6%
a 2944
 
4.6%
t 2733
 
4.2%
Other values (47) 17883
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35161
54.6%
Space Separator 15149
23.5%
Decimal Number 5509
 
8.6%
Other Punctuation 4794
 
7.5%
Uppercase Letter 2890
 
4.5%
Other Symbol 836
 
1.3%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4701
13.4%
o 4009
11.4%
i 3669
10.4%
n 3508
10.0%
d 2982
8.5%
b 2956
8.4%
a 2944
8.4%
t 2733
7.8%
r 1964
5.6%
s 1615
 
4.6%
Other values (15) 4080
11.6%
Uppercase Letter
ValueCountFrequency (%)
R 1727
59.8%
J 821
28.4%
C 91
 
3.1%
H 90
 
3.1%
S 74
 
2.6%
L 32
 
1.1%
T 15
 
0.5%
G 11
 
0.4%
B 10
 
0.3%
I 7
 
0.2%
Other values (5) 12
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 1614
29.3%
4 1015
18.4%
2 827
15.0%
3 433
 
7.9%
5 432
 
7.8%
8 294
 
5.3%
7 288
 
5.2%
6 210
 
3.8%
9 201
 
3.6%
0 195
 
3.5%
Other Punctuation
ValueCountFrequency (%)
· 3809
79.5%
. 982
 
20.5%
, 2
 
< 0.1%
/ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
15149
100.0%
Other Symbol
ValueCountFrequency (%)
836
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38051
59.1%
Common 26292
40.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4701
12.4%
o 4009
10.5%
i 3669
9.6%
n 3508
9.2%
d 2982
7.8%
b 2956
7.8%
a 2944
7.7%
t 2733
 
7.2%
r 1964
 
5.2%
R 1727
 
4.5%
Other values (30) 6858
18.0%
Common
ValueCountFrequency (%)
15149
57.6%
· 3809
 
14.5%
1 1614
 
6.1%
4 1015
 
3.9%
. 982
 
3.7%
836
 
3.2%
2 827
 
3.1%
3 433
 
1.6%
5 432
 
1.6%
8 294
 
1.1%
Other values (7) 901
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59695
92.8%
None 3812
 
5.9%
Misc Symbols 836
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15149
25.4%
e 4701
 
7.9%
o 4009
 
6.7%
i 3669
 
6.1%
n 3508
 
5.9%
d 2982
 
5.0%
b 2956
 
5.0%
a 2944
 
4.9%
t 2733
 
4.6%
r 1964
 
3.3%
Other values (43) 15080
25.3%
None
ValueCountFrequency (%)
· 3809
99.9%
á 2
 
0.1%
ç 1
 
< 0.1%
Misc Symbols
ValueCountFrequency (%)
836
100.0%
Distinct993
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:02:50.161131image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length1000
Median length1000
Mean length865.613
Min length7

Characters and Unicode

Total characters865613
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique990 ?
Unique (%)99.0%

Sample

1st rowthis is a big studio at the end of copacabana walking distance to arpoador and ipanema which can accomodate 4 persons 2 in a double bed and another 2 on couches which can be opened to sleep on br close to commercial area and public transportation br br b the space b br spacious flat with double bed air conditioner tv linen ceiling fan small kitchen and bathroom there is also 02 sofa bed which can accommodate 2 more people for an additional charge of us 20 each br br 24 hour doorman walking distance to ipanema the flat is very well located near the commercial area supermarkets restaurants banks bars and night clubs the very end of copacabana is a much better area to stay due to its proximity to ipanema you also have means of transport to anywhere in rio buses subway theres is a subway station two blocks away from the flat and taxis br br you are very close to the fortress of copacabana a main touristic attraction where you ha
2nd rowspecial location of the building on copacabana beach although the apartment does not have an ocean view but it s great for people who love the sea and for the ones staying for new year s accomodates up to 4 persons br br b the space b br spacious apartment with one bedroom comfortable double bed air conditioner tv linen living room with a double sofa bed 2 more people can be accommodated small kitchen bathroom ceiling fans 24 hour doorman walking distance to ipanema the building is on copacabana beach but the apartment does not have bech view it s at the back of the building facing the street behind br br the apartment is very well located near the commercial area supermarkets restaurants banks bars and night clubs the very end of copacabana is a much better area to stay due to its proximity to ipanema you are very close to the fortress of copacabana a main tourist attraction where you have the most fantastic view of the famous copacab
3rd rowaconchegante amplo b sico arejado iluminado com luz natural em pr dio seguro e familiar pr dio com portaria 24 horas e cameras de seguran a em todos os andares do edif cio tudo isto em copacabana a quase 1 quadra do mar o segundo pr dio da segunda quadra da praia est localizado na av prado junior quase esquina com av n sra de copacabana br br b the space b br o apartamento possui mob lia b sica mas a necess ria para voce se sentir em um espa o limpo confort vel e aconchegante tamb m tem os eletrodomesticos b sicos que n o podem faltar em um apto como microondas cafeteira el trica m quina de lavar fog o tv e geladeira todos a 110 volts e um guarda roupas grande onde voc pode colocar suas malas roupas e pertences na sala h uma mesa com 4 cadeiras um sof cama casal tipo fouton no quarto uma cama box de casal ortobom master pocket de molas ensacadas e 2 camas de solteiro uma delas ortobom e bicama o apto tamb m tem ar condicionado e ventil
4th rowplease note that elevated rates applies for new years and carnival price depends on length of stay and number of people generally i prefer a stay for 1 week or more and a maximum of 5 people 6 at the most contact me and we will discuss br bright and sunny br large balcony 25 square meters br high speed wifi up to 500mb br smart tv you can watch netflix etc if you have an account br 24h doorman br 1 minute to walk to copacabana beach br silent split air conditioning br best spot in rio br br b the space b br beautiful sunny 2 bedroom 90 square meters in 24h doorman building br 1 min to walk to copacabana beach br spacious living room 2 bedrooms with 2 full size beds each sleeps 2 br large balcony which looks out on pedestrian street br no traffic priceless in rio apts with sea view are noisy because of traffic br split air condition in each room almost silent like in a hotel br smart t
5th rowgorgeous apt 2 rooms 1 bedroom kitchen bathroom w luxury shower and non slip floor 2nd floor grill the big window of the room insulfilme the windows of the kitchen and living room ceiling fans in the rooms and single room 24 hour doorman close tr br br b the space b br rent apartment living room and bedroom that sleeps up to four people 2 sleeping in the room where there is a queen size bed 1 sleeping on the couch in the living room and bringing mat inflatable mattress fits most 1 pessoa tem 1 bathroom 1 kitchen and 1 more salinha o apartment is well located well there are 3 supermarkets nearby one just across the street many pharmacies restaurants bars gyms lottery 1 monday to saturday hospitais fica less than 8 minutes from lapa 10 min santa teresa 18 min center all done by p h shipping to all sides of the city room is spacious has a rack for hanging clothes and also a fan teto a cook has a reasonable the bathroom space is espa osoo doorman building
ValueCountFrequency (%)
br 9313
 
6.2%
the 6136
 
4.1%
and 4477
 
3.0%
a 3568
 
2.4%
b 2696
 
1.8%
de 2249
 
1.5%
in 2188
 
1.5%
to 2081
 
1.4%
with 2051
 
1.4%
of 1928
 
1.3%
Other values (8705) 113322
75.5%
2023-10-23T21:02:52.091926image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
209626
24.2%
e 70383
 
8.1%
a 69912
 
8.1%
o 55949
 
6.5%
r 51127
 
5.9%
t 48859
 
5.6%
i 43300
 
5.0%
n 40544
 
4.7%
s 39262
 
4.5%
c 26071
 
3.0%
Other values (28) 210580
24.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 648348
74.9%
Space Separator 209626
 
24.2%
Decimal Number 7633
 
0.9%
Connector Punctuation 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 70383
 
10.9%
a 69912
 
10.8%
o 55949
 
8.6%
r 51127
 
7.9%
t 48859
 
7.5%
i 43300
 
6.7%
n 40544
 
6.3%
s 39262
 
6.1%
c 26071
 
4.0%
d 25383
 
3.9%
Other values (16) 177558
27.4%
Decimal Number
ValueCountFrequency (%)
2 1833
24.0%
0 1473
19.3%
1 1298
17.0%
4 964
12.6%
5 687
 
9.0%
3 675
 
8.8%
6 222
 
2.9%
7 177
 
2.3%
8 171
 
2.2%
9 133
 
1.7%
Space Separator
ValueCountFrequency (%)
209626
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 648348
74.9%
Common 217265
 
25.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 70383
 
10.9%
a 69912
 
10.8%
o 55949
 
8.6%
r 51127
 
7.9%
t 48859
 
7.5%
i 43300
 
6.7%
n 40544
 
6.3%
s 39262
 
6.1%
c 26071
 
4.0%
d 25383
 
3.9%
Other values (16) 177558
27.4%
Common
ValueCountFrequency (%)
209626
96.5%
2 1833
 
0.8%
0 1473
 
0.7%
1 1298
 
0.6%
4 964
 
0.4%
5 687
 
0.3%
3 675
 
0.3%
6 222
 
0.1%
7 177
 
0.1%
8 171
 
0.1%
Other values (2) 139
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 865613
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
209626
24.2%
e 70383
 
8.1%
a 69912
 
8.1%
o 55949
 
6.5%
r 51127
 
5.9%
t 48859
 
5.6%
i 43300
 
5.0%
n 40544
 
4.7%
s 39262
 
4.5%
c 26071
 
3.0%
Other values (28) 210580
24.3%
Distinct536
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:02:53.037777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length1000
Median length951.5
Mean length201.407
Min length7

Characters and Unicode

Total characters201407
Distinct characters126
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique517 ?
Unique (%)51.7%

Sample

1st rowno_info
2nd rowno_info
3rd rowCopacabana, apelidada a princesinha do mar, faz juz ao apelido.<br />Além de possuir uma das praias mais famosas e charmosas do Rio de Janeiro fornece ao turista ampla estrutura com variedade de restaurantes, agências de turismos, casas de câmbio, supermercados, drogarias, e a poucos passos um grande shopping (Rio Sul) e etc.
4th rowThis is the one of the bests spots in Rio. Because of the large balcony and proximity to the beach, it has huge advantages in the current situation.
5th rowno_info
ValueCountFrequency (%)
the 1546
 
4.6%
and 1032
 
3.1%
a 824
 
2.5%
of 682
 
2.0%
de 632
 
1.9%
is 547
 
1.6%
to 527
 
1.6%
e 471
 
1.4%
in 464
 
1.4%
no_info 442
 
1.3%
Other values (4847) 26323
78.6%
2023-10-23T21:02:54.924529image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32753
16.3%
a 17542
 
8.7%
e 16529
 
8.2%
o 13903
 
6.9%
r 11007
 
5.5%
t 10953
 
5.4%
i 10519
 
5.2%
n 10379
 
5.2%
s 10253
 
5.1%
d 5715
 
2.8%
Other values (116) 61854
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 151596
75.3%
Space Separator 32759
 
16.3%
Uppercase Letter 7010
 
3.5%
Other Punctuation 6343
 
3.1%
Math Symbol 1648
 
0.8%
Decimal Number 953
 
0.5%
Connector Punctuation 444
 
0.2%
Dash Punctuation 231
 
0.1%
Open Punctuation 176
 
0.1%
Close Punctuation 172
 
0.1%
Other values (7) 75
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 17542
11.6%
e 16529
10.9%
o 13903
 
9.2%
r 11007
 
7.3%
t 10953
 
7.2%
i 10519
 
6.9%
n 10379
 
6.8%
s 10253
 
6.8%
d 5715
 
3.8%
l 5197
 
3.4%
Other values (33) 39599
26.1%
Uppercase Letter
ValueCountFrequency (%)
C 694
 
9.9%
R 600
 
8.6%
A 585
 
8.3%
T 562
 
8.0%
S 540
 
7.7%
I 512
 
7.3%
B 440
 
6.3%
L 413
 
5.9%
P 355
 
5.1%
O 282
 
4.0%
Other values (26) 2027
28.9%
Other Punctuation
ValueCountFrequency (%)
, 2847
44.9%
. 1819
28.7%
/ 906
 
14.3%
! 174
 
2.7%
" 164
 
2.6%
' 161
 
2.5%
: 114
 
1.8%
* 105
 
1.7%
; 28
 
0.4%
? 9
 
0.1%
Other values (5) 16
 
0.3%
Decimal Number
ValueCountFrequency (%)
0 198
20.8%
2 185
19.4%
1 173
18.2%
5 108
11.3%
4 101
10.6%
3 69
 
7.2%
7 37
 
3.9%
6 30
 
3.1%
9 28
 
2.9%
8 24
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 225
97.4%
4
 
1.7%
2
 
0.9%
Space Separator
ValueCountFrequency (%)
32753
> 99.9%
  6
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
> 824
50.0%
< 824
50.0%
Open Punctuation
ValueCountFrequency (%)
( 175
99.4%
1
 
0.6%
Final Punctuation
ValueCountFrequency (%)
23
76.7%
7
 
23.3%
Modifier Symbol
ValueCountFrequency (%)
´ 14
87.5%
` 2
 
12.5%
Initial Punctuation
ValueCountFrequency (%)
7
87.5%
1
 
12.5%
Other Symbol
ValueCountFrequency (%)
6
66.7%
3
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 444
100.0%
Close Punctuation
ValueCountFrequency (%)
) 172
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 6
100.0%
Format
ValueCountFrequency (%)
4
100.0%
Other Letter
ValueCountFrequency (%)
º 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 158608
78.7%
Common 42799
 
21.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 17542
 
11.1%
e 16529
 
10.4%
o 13903
 
8.8%
r 11007
 
6.9%
t 10953
 
6.9%
i 10519
 
6.6%
n 10379
 
6.5%
s 10253
 
6.5%
d 5715
 
3.6%
l 5197
 
3.3%
Other values (70) 46611
29.4%
Common
ValueCountFrequency (%)
32753
76.5%
, 2847
 
6.7%
. 1819
 
4.3%
/ 906
 
2.1%
> 824
 
1.9%
< 824
 
1.9%
_ 444
 
1.0%
- 225
 
0.5%
0 198
 
0.5%
2 185
 
0.4%
Other values (36) 1774
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199730
99.2%
None 1612
 
0.8%
Punctuation 56
 
< 0.1%
Misc Symbols 6
 
< 0.1%
Geometric Shapes 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32753
16.4%
a 17542
 
8.8%
e 16529
 
8.3%
o 13903
 
7.0%
r 11007
 
5.5%
t 10953
 
5.5%
i 10519
 
5.3%
n 10379
 
5.2%
s 10253
 
5.1%
d 5715
 
2.9%
Other values (73) 60177
30.1%
None
ValueCountFrequency (%)
é 395
24.5%
á 245
15.2%
ã 230
14.3%
ç 188
11.7%
ó 116
 
7.2%
í 83
 
5.1%
ê 67
 
4.2%
ô 65
 
4.0%
ú 57
 
3.5%
â 36
 
2.2%
Other values (21) 130
 
8.1%
Punctuation
ValueCountFrequency (%)
23
41.1%
7
 
12.5%
7
 
12.5%
6
 
10.7%
4
 
7.1%
4
 
7.1%
2
 
3.6%
1
 
1.8%
1
 
1.8%
1
 
1.8%
Misc Symbols
ValueCountFrequency (%)
6
100.0%
Geometric Shapes
ValueCountFrequency (%)
3
100.0%

picture_url
Text

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:02:55.748945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length111
Median length110
Mean length72.24
Min length61

Characters and Unicode

Total characters72240
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st rowhttps://a0.muscache.com/pictures/3582382/ee8acc55_original.jpg
2nd rowhttps://a0.muscache.com/pictures/3671683/d74b44a4_original.jpg
3rd rowhttps://a0.muscache.com/pictures/5725a59b-147d-4bf2-99f2-ba67f55ee770.jpg
4th rowhttps://a0.muscache.com/pictures/65320518/30698f38_original.jpg
5th rowhttps://a0.muscache.com/pictures/13585778/f2f26a80_original.jpg
ValueCountFrequency (%)
https://a0.muscache.com/pictures/3582382/ee8acc55_original.jpg 1
 
0.1%
https://a0.muscache.com/pictures/5983438/5e6a7829_original.jpg 1
 
0.1%
https://a0.muscache.com/pictures/831828/01a97dd6_original.jpg 1
 
0.1%
https://a0.muscache.com/pictures/3791133/818eecb2_original.jpg 1
 
0.1%
https://a0.muscache.com/pictures/5725a59b-147d-4bf2-99f2-ba67f55ee770.jpg 1
 
0.1%
https://a0.muscache.com/pictures/65320518/30698f38_original.jpg 1
 
0.1%
https://a0.muscache.com/pictures/13585778/f2f26a80_original.jpg 1
 
0.1%
https://a0.muscache.com/pictures/a745aa21-b8dd-4959-a040-eb8e6e6f07ee.jpg 1
 
0.1%
https://a0.muscache.com/pictures/46b95cc9-d4d6-4a90-b444-5ddaf9ecca11.jpg 1
 
0.1%
https://a0.muscache.com/pictures/12451384/18c18103_original.jpg 1
 
0.1%
Other values (990) 990
99.0%
2023-10-23T21:02:57.279874image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 5064
 
7.0%
/ 4987
 
6.9%
a 3962
 
5.5%
s 3285
 
4.5%
e 3271
 
4.5%
t 3177
 
4.4%
p 3053
 
4.2%
. 2999
 
4.2%
i 2674
 
3.7%
0 2644
 
3.7%
Other values (27) 37124
51.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42233
58.5%
Decimal Number 18316
25.4%
Other Punctuation 8986
 
12.4%
Dash Punctuation 2016
 
2.8%
Connector Punctuation 540
 
0.7%
Uppercase Letter 149
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 5064
12.0%
a 3962
 
9.4%
s 3285
 
7.8%
e 3271
 
7.7%
t 3177
 
7.5%
p 3053
 
7.2%
i 2674
 
6.3%
m 2111
 
5.0%
h 2027
 
4.8%
o 2013
 
4.8%
Other values (10) 11596
27.5%
Decimal Number
ValueCountFrequency (%)
0 2644
14.4%
4 2058
11.2%
1 1846
10.1%
2 1784
9.7%
8 1749
9.5%
3 1689
9.2%
9 1688
9.2%
6 1651
9.0%
7 1617
8.8%
5 1590
8.7%
Other Punctuation
ValueCountFrequency (%)
/ 4987
55.5%
. 2999
33.4%
: 1000
 
11.1%
Uppercase Letter
ValueCountFrequency (%)
H 148
99.3%
S 1
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 2016
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 540
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42382
58.7%
Common 29858
41.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 5064
11.9%
a 3962
 
9.3%
s 3285
 
7.8%
e 3271
 
7.7%
t 3177
 
7.5%
p 3053
 
7.2%
i 2674
 
6.3%
m 2111
 
5.0%
h 2027
 
4.8%
o 2013
 
4.7%
Other values (12) 11745
27.7%
Common
ValueCountFrequency (%)
/ 4987
16.7%
. 2999
10.0%
0 2644
8.9%
4 2058
 
6.9%
- 2016
 
6.8%
1 1846
 
6.2%
2 1784
 
6.0%
8 1749
 
5.9%
3 1689
 
5.7%
9 1688
 
5.7%
Other values (5) 6398
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 5064
 
7.0%
/ 4987
 
6.9%
a 3962
 
5.5%
s 3285
 
4.5%
e 3271
 
4.5%
t 3177
 
4.4%
p 3053
 
4.2%
. 2999
 
4.2%
i 2674
 
3.7%
0 2644
 
3.7%
Other values (27) 37124
51.4%

host_id
Real number (ℝ)

SKEWED 

Distinct766
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5260361.309
Minimum34105
Maximum380059214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:02:58.051922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum34105
5-th percentile406989
Q11601126.5
median4300968.5
Q37505557.75
95-th percentile10624379.75
Maximum380059214
Range380025109
Interquartile range (IQR)5904431.25

Descriptive statistics

Standard deviation13144517.29
Coefficient of variation (CV)2.498786019
Kurtosis668.311355
Mean5260361.309
Median Absolute Deviation (MAD)2797345
Skewness24.01751487
Sum5260361309
Variance1.727783349 × 1014
MonotonicityNot monotonic
2023-10-23T21:02:58.978962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4307081 23
 
2.3%
9270905 12
 
1.2%
792218 9
 
0.9%
5165851 7
 
0.7%
1429181 7
 
0.7%
449677 6
 
0.6%
4581272 5
 
0.5%
1603206 5
 
0.5%
3962758 5
 
0.5%
1648634 5
 
0.5%
Other values (756) 916
91.6%
ValueCountFrequency (%)
34105 1
0.1%
64036 2
0.2%
68997 1
0.1%
70933 2
0.2%
90350 1
0.1%
ValueCountFrequency (%)
380059214 1
 
0.1%
115318144 1
 
0.1%
56451628 3
0.3%
14510615 1
 
0.1%
11661949 1
 
0.1%
Distinct766
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:02:59.957595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length43
Median length41
Mean length40.941
Min length39

Characters and Unicode

Total characters40941
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique644 ?
Unique (%)64.4%

Sample

1st rowhttps://www.airbnb.com/users/show/1207700
2nd rowhttps://www.airbnb.com/users/show/1207700
3rd rowhttps://www.airbnb.com/users/show/1241662
4th rowhttps://www.airbnb.com/users/show/68997
5th rowhttps://www.airbnb.com/users/show/2649464
ValueCountFrequency (%)
https://www.airbnb.com/users/show/4307081 23
 
2.3%
https://www.airbnb.com/users/show/9270905 12
 
1.2%
https://www.airbnb.com/users/show/792218 9
 
0.9%
https://www.airbnb.com/users/show/5165851 7
 
0.7%
https://www.airbnb.com/users/show/1429181 7
 
0.7%
https://www.airbnb.com/users/show/449677 6
 
0.6%
https://www.airbnb.com/users/show/4581272 5
 
0.5%
https://www.airbnb.com/users/show/1603206 5
 
0.5%
https://www.airbnb.com/users/show/3962758 5
 
0.5%
https://www.airbnb.com/users/show/1648634 5
 
0.5%
Other values (756) 916
91.6%
2023-10-23T21:03:01.792196image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 5000
 
12.2%
s 4000
 
9.8%
w 4000
 
9.8%
h 2000
 
4.9%
r 2000
 
4.9%
t 2000
 
4.9%
b 2000
 
4.9%
o 2000
 
4.9%
. 2000
 
4.9%
a 1000
 
2.4%
Other values (18) 14941
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26000
63.5%
Other Punctuation 8000
 
19.5%
Decimal Number 6941
 
17.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 4000
15.4%
w 4000
15.4%
h 2000
 
7.7%
r 2000
 
7.7%
t 2000
 
7.7%
b 2000
 
7.7%
o 2000
 
7.7%
a 1000
 
3.8%
n 1000
 
3.8%
c 1000
 
3.8%
Other values (5) 5000
19.2%
Decimal Number
ValueCountFrequency (%)
1 848
12.2%
0 737
10.6%
4 724
10.4%
5 704
10.1%
2 691
10.0%
6 690
9.9%
9 662
9.5%
8 654
9.4%
3 629
9.1%
7 602
8.7%
Other Punctuation
ValueCountFrequency (%)
/ 5000
62.5%
. 2000
 
25.0%
: 1000
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 26000
63.5%
Common 14941
36.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 4000
15.4%
w 4000
15.4%
h 2000
 
7.7%
r 2000
 
7.7%
t 2000
 
7.7%
b 2000
 
7.7%
o 2000
 
7.7%
a 1000
 
3.8%
n 1000
 
3.8%
c 1000
 
3.8%
Other values (5) 5000
19.2%
Common
ValueCountFrequency (%)
/ 5000
33.5%
. 2000
 
13.4%
: 1000
 
6.7%
1 848
 
5.7%
0 737
 
4.9%
4 724
 
4.8%
5 704
 
4.7%
2 691
 
4.6%
6 690
 
4.6%
9 662
 
4.4%
Other values (3) 1885
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 5000
 
12.2%
s 4000
 
9.8%
w 4000
 
9.8%
h 2000
 
4.9%
r 2000
 
4.9%
t 2000
 
4.9%
b 2000
 
4.9%
o 2000
 
4.9%
. 2000
 
4.9%
a 1000
 
2.4%
Other values (18) 14941
36.5%
Distinct515
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:02.774437image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length28
Median length19
Mean length7.291
Min length2

Characters and Unicode

Total characters7291
Distinct characters73
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique329 ?
Unique (%)32.9%

Sample

1st rowMaria Luiza
2nd rowMaria Luiza
3rd rowNilda
4th rowMatthias
5th rowLélla
ValueCountFrequency (%)
36
 
2.9%
maria 34
 
2.7%
carlos 25
 
2.0%
a 23
 
1.8%
nereu 23
 
1.8%
ana 18
 
1.4%
e 15
 
1.2%
paulo 14
 
1.1%
marcia 13
 
1.0%
silvia 11
 
0.9%
Other values (504) 1045
83.1%
2023-10-23T21:03:04.693308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1133
15.5%
i 657
 
9.0%
e 620
 
8.5%
r 513
 
7.0%
n 451
 
6.2%
o 427
 
5.9%
l 418
 
5.7%
262
 
3.6%
s 244
 
3.3%
u 215
 
2.9%
Other values (63) 2351
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5745
78.8%
Uppercase Letter 1222
 
16.8%
Space Separator 262
 
3.6%
Other Punctuation 34
 
0.5%
Decimal Number 8
 
0.1%
Close Punctuation 5
 
0.1%
Open Punctuation 5
 
0.1%
Other Symbol 4
 
0.1%
Nonspacing Mark 4
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1133
19.7%
i 657
11.4%
e 620
10.8%
r 513
8.9%
n 451
 
7.9%
o 427
 
7.4%
l 418
 
7.3%
s 244
 
4.2%
u 215
 
3.7%
d 171
 
3.0%
Other values (25) 896
15.6%
Uppercase Letter
ValueCountFrequency (%)
M 150
12.3%
A 132
 
10.8%
C 100
 
8.2%
R 90
 
7.4%
L 89
 
7.3%
E 67
 
5.5%
P 61
 
5.0%
S 60
 
4.9%
D 57
 
4.7%
G 55
 
4.5%
Other values (15) 361
29.5%
Other Punctuation
ValueCountFrequency (%)
& 26
76.5%
/ 6
 
17.6%
, 1
 
2.9%
' 1
 
2.9%
Decimal Number
ValueCountFrequency (%)
8 4
50.0%
4 4
50.0%
Space Separator
ValueCountFrequency (%)
262
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Other Symbol
ValueCountFrequency (%)
4
100.0%
Nonspacing Mark
ValueCountFrequency (%)
4
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6967
95.6%
Common 320
 
4.4%
Inherited 4
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1133
16.3%
i 657
 
9.4%
e 620
 
8.9%
r 513
 
7.4%
n 451
 
6.5%
o 427
 
6.1%
l 418
 
6.0%
s 244
 
3.5%
u 215
 
3.1%
d 171
 
2.5%
Other values (50) 2118
30.4%
Common
ValueCountFrequency (%)
262
81.9%
& 26
 
8.1%
/ 6
 
1.9%
) 5
 
1.6%
( 5
 
1.6%
4
 
1.2%
8 4
 
1.2%
4 4
 
1.2%
+ 1
 
0.3%
- 1
 
0.3%
Other values (2) 2
 
0.6%
Inherited
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7225
99.1%
None 58
 
0.8%
Dingbats 4
 
0.1%
VS 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1133
15.7%
i 657
 
9.1%
e 620
 
8.6%
r 513
 
7.1%
n 451
 
6.2%
o 427
 
5.9%
l 418
 
5.8%
262
 
3.6%
s 244
 
3.4%
u 215
 
3.0%
Other values (51) 2285
31.6%
None
ValueCountFrequency (%)
é 24
41.4%
á 14
24.1%
ã 6
 
10.3%
í 4
 
6.9%
É 3
 
5.2%
â 2
 
3.4%
ô 2
 
3.4%
õ 1
 
1.7%
ê 1
 
1.7%
ç 1
 
1.7%
Dingbats
ValueCountFrequency (%)
4
100.0%
VS
ValueCountFrequency (%)
4
100.0%
Distinct522
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2009-08-26 00:00:00
Maximum2020-12-14 00:00:00
2023-10-23T21:03:05.576834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-23T21:03:06.690141image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct65
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:07.765138image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length31
Median length22
Mean length20.12
Min length6

Characters and Unicode

Total characters20120
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)3.8%

Sample

1st rowRio de Janeiro, Brazil
2nd rowRio de Janeiro, Brazil
3rd rowRio de Janeiro, Brazil
4th rowRio de Janeiro, Brazil
5th rowRio de Janeiro, Brazil
ValueCountFrequency (%)
brazil 899
25.0%
rio 830
23.1%
de 788
21.9%
janeiro 788
21.9%
no_info 25
 
0.7%
paulo 16
 
0.4%
são 15
 
0.4%
state 13
 
0.4%
of 13
 
0.4%
weehawken 12
 
0.3%
Other values (95) 196
 
5.5%
2023-10-23T21:03:09.583863image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 2626
13.1%
2595
12.9%
a 1833
9.1%
o 1779
8.8%
r 1756
8.7%
e 1728
8.6%
l 959
 
4.8%
, 936
 
4.7%
n 934
 
4.6%
B 918
 
4.6%
Other values (46) 4056
20.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13761
68.4%
Uppercase Letter 2803
 
13.9%
Space Separator 2595
 
12.9%
Other Punctuation 936
 
4.7%
Connector Punctuation 25
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2626
19.1%
a 1833
13.3%
o 1779
12.9%
r 1756
12.8%
e 1728
12.6%
l 959
 
7.0%
n 934
 
6.8%
z 904
 
6.6%
d 825
 
6.0%
t 83
 
0.6%
Other values (19) 334
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
B 918
32.8%
R 831
29.6%
J 802
28.6%
S 46
 
1.6%
A 30
 
1.1%
P 27
 
1.0%
N 24
 
0.9%
M 17
 
0.6%
W 14
 
0.5%
U 11
 
0.4%
Other values (14) 83
 
3.0%
Space Separator
ValueCountFrequency (%)
2595
100.0%
Other Punctuation
ValueCountFrequency (%)
, 936
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16564
82.3%
Common 3556
 
17.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2626
15.9%
a 1833
11.1%
o 1779
10.7%
r 1756
10.6%
e 1728
10.4%
l 959
 
5.8%
n 934
 
5.6%
B 918
 
5.5%
z 904
 
5.5%
R 831
 
5.0%
Other values (43) 2296
13.9%
Common
ValueCountFrequency (%)
2595
73.0%
, 936
 
26.3%
_ 25
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20092
99.9%
None 28
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 2626
13.1%
2595
12.9%
a 1833
9.1%
o 1779
8.9%
r 1756
8.7%
e 1728
8.6%
l 959
 
4.8%
, 936
 
4.7%
n 934
 
4.6%
B 918
 
4.6%
Other values (42) 4028
20.0%
None
ValueCountFrequency (%)
ã 16
57.1%
í 5
 
17.9%
ó 4
 
14.3%
é 3
 
10.7%
Distinct700
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:10.589566image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6323
Median length645
Mean length355.861
Min length1

Characters and Unicode

Total characters355861
Distinct characters131
Distinct categories17 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique579 ?
Unique (%)57.9%

Sample

1st rowMeu nome é Maria Luiza, adoro ajudar meus hóspedes, pois vivi muito tempo no exterior, falo várias línguas, e entendo como é viver fora da sua cidade. Falo e escrevo em Inglês, francês, espanhol e português.
2nd rowMeu nome é Maria Luiza, adoro ajudar meus hóspedes, pois vivi muito tempo no exterior, falo várias línguas, e entendo como é viver fora da sua cidade. Falo e escrevo em Inglês, francês, espanhol e português.
3rd rowHellow ! Im Nilda! I love Rio de Janeiro. I work renting apartments for short time. the places are simples! but very clean , safe and well provided with basic staffs to spent a great vacations. Very well located, next to the beach one of the most famous Rio de Janeiro´ s beach: Copacabana you ll have easy and plenty access by bus and others public transportations services to the main and classic touristic points more visited by the travellers in Rio de Janeiro. Welcome to Rio, welcome to Brazil!
4th rowI am a journalist/writer. Lived in NYC for 15 years. I am now based in Rio and published 3 volumes of travel stories on AMAZ0N: "The World Is My Oyster". If you have never been to Rio, check out the first story, and you'll get an idea. Apart from Rio, you'll find 29 other travel stories from all around the globe.
5th rowOlá , meu nome é Lélla . Sejam todos muito bem vindos a cidade maravilhosa do Rio de Janeiro . Obrigada por verificarem meu anúncio .
ValueCountFrequency (%)
de 1732
 
2.8%
e 1645
 
2.7%
and 1472
 
2.4%
a 1297
 
2.1%
i 1086
 
1.8%
to 1028
 
1.7%
the 915
 
1.5%
in 906
 
1.5%
rio 894
 
1.5%
759
 
1.2%
Other values (6916) 49278
80.8%
2023-10-23T21:03:12.568172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
60086
16.9%
e 32251
 
9.1%
a 29644
 
8.3%
o 25999
 
7.3%
i 19394
 
5.4%
s 18272
 
5.1%
r 18014
 
5.1%
n 17021
 
4.8%
t 15765
 
4.4%
d 11152
 
3.1%
Other values (121) 108263
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 265879
74.7%
Space Separator 60095
 
16.9%
Uppercase Letter 12679
 
3.6%
Other Punctuation 10034
 
2.8%
Control 4629
 
1.3%
Decimal Number 1188
 
0.3%
Dash Punctuation 636
 
0.2%
Close Punctuation 231
 
0.1%
Open Punctuation 191
 
0.1%
Connector Punctuation 132
 
< 0.1%
Other values (7) 167
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 32251
12.1%
a 29644
11.1%
o 25999
 
9.8%
i 19394
 
7.3%
s 18272
 
6.9%
r 18014
 
6.8%
n 17021
 
6.4%
t 15765
 
5.9%
d 11152
 
4.2%
m 10194
 
3.8%
Other values (34) 68173
25.6%
Uppercase Letter
ValueCountFrequency (%)
I 1802
14.2%
R 1276
 
10.1%
A 1044
 
8.2%
S 988
 
7.8%
E 752
 
5.9%
C 736
 
5.8%
M 604
 
4.8%
J 565
 
4.5%
O 562
 
4.4%
B 561
 
4.4%
Other values (27) 3789
29.9%
Other Punctuation
ValueCountFrequency (%)
, 4360
43.5%
. 4023
40.1%
! 716
 
7.1%
' 384
 
3.8%
: 125
 
1.2%
" 99
 
1.0%
? 88
 
0.9%
/ 88
 
0.9%
* 67
 
0.7%
; 40
 
0.4%
Other values (6) 44
 
0.4%
Decimal Number
ValueCountFrequency (%)
0 320
26.9%
2 224
18.9%
1 187
15.7%
3 97
 
8.2%
5 82
 
6.9%
4 71
 
6.0%
9 69
 
5.8%
7 58
 
4.9%
6 42
 
3.5%
8 38
 
3.2%
Math Symbol
ValueCountFrequency (%)
= 59
86.8%
+ 6
 
8.8%
> 2
 
2.9%
| 1
 
1.5%
Open Punctuation
ValueCountFrequency (%)
( 185
96.9%
[ 4
 
2.1%
2
 
1.0%
Space Separator
ValueCountFrequency (%)
60086
> 99.9%
  9
 
< 0.1%
Control
ValueCountFrequency (%)
2677
57.8%
1952
42.2%
Close Punctuation
ValueCountFrequency (%)
) 227
98.3%
] 4
 
1.7%
Final Punctuation
ValueCountFrequency (%)
40
75.5%
13
 
24.5%
Modifier Symbol
ValueCountFrequency (%)
´ 24
88.9%
` 3
 
11.1%
Initial Punctuation
ValueCountFrequency (%)
11
84.6%
2
 
15.4%
Dash Punctuation
ValueCountFrequency (%)
- 636
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 132
100.0%
Other Letter
ValueCountFrequency (%)
º 3
100.0%
Private Use
ValueCountFrequency (%)
2
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 278561
78.3%
Common 77298
 
21.7%
Unknown 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 32251
 
11.6%
a 29644
 
10.6%
o 25999
 
9.3%
i 19394
 
7.0%
s 18272
 
6.6%
r 18014
 
6.5%
n 17021
 
6.1%
t 15765
 
5.7%
d 11152
 
4.0%
m 10194
 
3.7%
Other values (72) 80855
29.0%
Common
ValueCountFrequency (%)
60086
77.7%
, 4360
 
5.6%
. 4023
 
5.2%
2677
 
3.5%
1952
 
2.5%
! 716
 
0.9%
- 636
 
0.8%
' 384
 
0.5%
0 320
 
0.4%
) 227
 
0.3%
Other values (38) 1917
 
2.5%
Unknown
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 352150
99.0%
None 3638
 
1.0%
Punctuation 71
 
< 0.1%
PUA 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
60086
17.1%
e 32251
 
9.2%
a 29644
 
8.4%
o 25999
 
7.4%
i 19394
 
5.5%
s 18272
 
5.2%
r 18014
 
5.1%
n 17021
 
4.8%
t 15765
 
4.5%
d 11152
 
3.2%
Other values (80) 104552
29.7%
None
ValueCountFrequency (%)
é 661
18.2%
á 582
16.0%
ã 510
14.0%
ç 483
13.3%
ó 326
9.0%
ê 311
8.5%
í 236
 
6.5%
ú 125
 
3.4%
à 98
 
2.7%
õ 83
 
2.3%
Other values (24) 223
 
6.1%
Punctuation
ValueCountFrequency (%)
40
56.3%
13
 
18.3%
11
 
15.5%
3
 
4.2%
2
 
2.8%
2
 
2.8%
PUA
ValueCountFrequency (%)
2
100.0%
Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:13.130939image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length18
Median length14
Mean length13.694
Min length7

Characters and Unicode

Total characters13694
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwithin a few hours
2nd rowwithin a few hours
3rd rowwithin an hour
4th rowwithin an hour
5th rowno_info
ValueCountFrequency (%)
within 767
25.7%
an 454
15.2%
hour 454
15.2%
a 372
12.5%
few 276
 
9.2%
hours 217
 
7.3%
no_info 174
 
5.8%
day 96
 
3.2%
days 59
 
2.0%
or 59
 
2.0%
2023-10-23T21:03:14.456195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1987
14.5%
i 1708
12.5%
n 1569
11.5%
h 1438
10.5%
o 1137
8.3%
w 1043
7.6%
a 981
7.2%
r 789
 
5.8%
t 767
 
5.6%
u 671
 
4.9%
Other values (7) 1604
11.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11533
84.2%
Space Separator 1987
 
14.5%
Connector Punctuation 174
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1708
14.8%
n 1569
13.6%
h 1438
12.5%
o 1137
9.9%
w 1043
9.0%
a 981
8.5%
r 789
6.8%
t 767
6.7%
u 671
 
5.8%
f 450
 
3.9%
Other values (5) 980
8.5%
Space Separator
ValueCountFrequency (%)
1987
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11533
84.2%
Common 2161
 
15.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 1708
14.8%
n 1569
13.6%
h 1438
12.5%
o 1137
9.9%
w 1043
9.0%
a 981
8.5%
r 789
6.8%
t 767
6.7%
u 671
 
5.8%
f 450
 
3.9%
Other values (5) 980
8.5%
Common
ValueCountFrequency (%)
1987
91.9%
_ 174
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1987
14.5%
i 1708
12.5%
n 1569
11.5%
h 1438
10.5%
o 1137
8.3%
w 1043
7.6%
a 981
7.2%
r 789
 
5.8%
t 767
 
5.6%
u 671
 
4.9%
Other values (7) 1604
11.7%
Distinct40
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:15.034406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.239
Min length2

Characters and Unicode

Total characters4239
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)1.1%

Sample

1st row100%
2nd row100%
3rd row100%
4th row100%
5th rowno_info
ValueCountFrequency (%)
100 579
57.9%
no_info 174
 
17.4%
96 38
 
3.8%
0 36
 
3.6%
90 31
 
3.1%
50 15
 
1.5%
94 14
 
1.4%
80 13
 
1.3%
98 13
 
1.3%
70 8
 
0.8%
Other values (30) 79
 
7.9%
2023-10-23T21:03:16.266754image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1274
30.1%
% 826
19.5%
1 585
13.8%
n 348
 
8.2%
o 348
 
8.2%
_ 174
 
4.1%
i 174
 
4.1%
f 174
 
4.1%
9 129
 
3.0%
6 54
 
1.3%
Other values (6) 153
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2195
51.8%
Lowercase Letter 1044
24.6%
Other Punctuation 826
 
19.5%
Connector Punctuation 174
 
4.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1274
58.0%
1 585
26.7%
9 129
 
5.9%
6 54
 
2.5%
8 42
 
1.9%
7 33
 
1.5%
5 22
 
1.0%
4 22
 
1.0%
3 21
 
1.0%
2 13
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
n 348
33.3%
o 348
33.3%
i 174
16.7%
f 174
16.7%
Other Punctuation
ValueCountFrequency (%)
% 826
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3195
75.4%
Latin 1044
 
24.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1274
39.9%
% 826
25.9%
1 585
18.3%
_ 174
 
5.4%
9 129
 
4.0%
6 54
 
1.7%
8 42
 
1.3%
7 33
 
1.0%
5 22
 
0.7%
4 22
 
0.7%
Other values (2) 34
 
1.1%
Latin
ValueCountFrequency (%)
n 348
33.3%
o 348
33.3%
i 174
16.7%
f 174
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1274
30.1%
% 826
19.5%
1 585
13.8%
n 348
 
8.2%
o 348
 
8.2%
_ 174
 
4.1%
i 174
 
4.1%
f 174
 
4.1%
9 129
 
3.0%
6 54
 
1.3%
Other values (6) 153
 
3.6%
Distinct78
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:17.116803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.694
Min length2

Characters and Unicode

Total characters3694
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)1.4%

Sample

1st row82%
2nd row82%
3rd row96%
4th row96%
5th row0%
ValueCountFrequency (%)
100 206
20.6%
no_info 139
 
13.9%
0 64
 
6.4%
99 39
 
3.9%
97 34
 
3.4%
96 31
 
3.1%
50 30
 
3.0%
25 27
 
2.7%
98 26
 
2.6%
94 23
 
2.3%
Other values (68) 381
38.1%
2023-10-23T21:03:18.532154image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 861
23.3%
0 555
15.0%
9 302
 
8.2%
n 278
 
7.5%
o 278
 
7.5%
1 250
 
6.8%
7 162
 
4.4%
8 160
 
4.3%
_ 139
 
3.8%
i 139
 
3.8%
Other values (6) 570
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1860
50.4%
Other Punctuation 861
23.3%
Lowercase Letter 834
22.6%
Connector Punctuation 139
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 555
29.8%
9 302
16.2%
1 250
13.4%
7 162
 
8.7%
8 160
 
8.6%
5 112
 
6.0%
6 99
 
5.3%
2 90
 
4.8%
3 66
 
3.5%
4 64
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
n 278
33.3%
o 278
33.3%
i 139
16.7%
f 139
16.7%
Other Punctuation
ValueCountFrequency (%)
% 861
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2860
77.4%
Latin 834
 
22.6%

Most frequent character per script

Common
ValueCountFrequency (%)
% 861
30.1%
0 555
19.4%
9 302
 
10.6%
1 250
 
8.7%
7 162
 
5.7%
8 160
 
5.6%
_ 139
 
4.9%
5 112
 
3.9%
6 99
 
3.5%
2 90
 
3.1%
Other values (2) 130
 
4.5%
Latin
ValueCountFrequency (%)
n 278
33.3%
o 278
33.3%
i 139
16.7%
f 139
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 861
23.3%
0 555
15.0%
9 302
 
8.2%
n 278
 
7.5%
o 278
 
7.5%
1 250
 
6.8%
7 162
 
4.4%
8 160
 
4.3%
_ 139
 
3.8%
i 139
 
3.8%
Other values (6) 570
15.4%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
671 
True
329 
ValueCountFrequency (%)
False 671
67.1%
True 329
32.9%
2023-10-23T21:03:19.188791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct766
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:19.940787image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length130
Median length129
Mean length104.394
Min length99

Characters and Unicode

Total characters104394
Distinct characters39
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique644 ?
Unique (%)64.4%

Sample

1st rowhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_small
2nd rowhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_small
3rd rowhttps://a0.muscache.com/im/pictures/user/fea78163-5495-401a-a620-ed948f59ac91.jpg?aki_policy=profile_small
4th rowhttps://a0.muscache.com/im/pictures/user/67b13cea-8c11-49c0-a08d-7f42c330676e.jpg?aki_policy=profile_small
5th rowhttps://a0.muscache.com/im/users/2649464/profile_pic/1339814302/original.jpg?aki_policy=profile_small
ValueCountFrequency (%)
https://a0.muscache.com/im/pictures/user/6c5b1dec-21b3-4fdf-abc8-787d2fa9bde4.jpg?aki_policy=profile_small 23
 
2.3%
https://a0.muscache.com/im/users/9270905/profile_pic/1381110921/original.jpg?aki_policy=profile_small 12
 
1.2%
https://a0.muscache.com/im/users/792218/profile_pic/1310233853/original.jpg?aki_policy=profile_small 9
 
0.9%
https://a0.muscache.com/im/users/5165851/profile_pic/1362412775/original.jpg?aki_policy=profile_small 7
 
0.7%
https://a0.muscache.com/im/pictures/user/e7e2f4b9-91bd-42e0-91f4-c8497791b8c9.jpg?aki_policy=profile_small 7
 
0.7%
https://a0.muscache.com/im/pictures/user/b6ae525a-fd1d-451f-922e-8ee6dfa2a16f.jpg?aki_policy=profile_small 6
 
0.6%
https://a0.muscache.com/im/pictures/user/1d3acad1-e823-4577-993f-b953a4c514e5.jpg?aki_policy=profile_small 5
 
0.5%
https://a0.muscache.com/im/pictures/user/d9351e88-fd2a-42b7-9c7a-1eb3caffd888.jpg?aki_policy=profile_small 5
 
0.5%
https://a0.muscache.com/im/pictures/user/7e48ab44-bcf9-4f50-aae5-dfdf852b0932.jpg?aki_policy=profile_small 5
 
0.5%
https://a0.muscache.com/im/users/1648634/profile_pic/1327454133/original.jpg?aki_policy=profile_small 5
 
0.5%
Other values (756) 916
91.6%
2023-10-23T21:03:21.530866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 7104
 
6.8%
i 6614
 
6.3%
c 5924
 
5.7%
a 5628
 
5.4%
p 5510
 
5.3%
l 5062
 
4.8%
s 5042
 
4.8%
e 5025
 
4.8%
o 4062
 
3.9%
m 4000
 
3.8%
Other values (29) 50423
48.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 67080
64.3%
Decimal Number 19656
 
18.8%
Other Punctuation 12104
 
11.6%
Connector Punctuation 2510
 
2.4%
Dash Punctuation 2002
 
1.9%
Math Symbol 1000
 
1.0%
Uppercase Letter 42
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 6614
 
9.9%
c 5924
 
8.8%
a 5628
 
8.4%
p 5510
 
8.2%
l 5062
 
7.5%
s 5042
 
7.5%
e 5025
 
7.5%
o 4062
 
6.1%
m 4000
 
6.0%
r 3594
 
5.4%
Other values (11) 16619
24.8%
Decimal Number
ValueCountFrequency (%)
0 2637
13.4%
1 2351
12.0%
4 2212
11.3%
3 2038
10.4%
8 1848
9.4%
9 1767
9.0%
7 1732
8.8%
5 1717
8.7%
2 1710
8.7%
6 1644
8.4%
Other Punctuation
ValueCountFrequency (%)
/ 7104
58.7%
. 3000
24.8%
? 1000
 
8.3%
: 1000
 
8.3%
Connector Punctuation
ValueCountFrequency (%)
_ 2510
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2002
100.0%
Math Symbol
ValueCountFrequency (%)
= 1000
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 67122
64.3%
Common 37272
35.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6614
 
9.9%
c 5924
 
8.8%
a 5628
 
8.4%
p 5510
 
8.2%
l 5062
 
7.5%
s 5042
 
7.5%
e 5025
 
7.5%
o 4062
 
6.1%
m 4000
 
6.0%
r 3594
 
5.4%
Other values (12) 16661
24.8%
Common
ValueCountFrequency (%)
/ 7104
19.1%
. 3000
 
8.0%
0 2637
 
7.1%
_ 2510
 
6.7%
1 2351
 
6.3%
4 2212
 
5.9%
3 2038
 
5.5%
- 2002
 
5.4%
8 1848
 
5.0%
9 1767
 
4.7%
Other values (7) 9803
26.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 7104
 
6.8%
i 6614
 
6.3%
c 5924
 
5.7%
a 5628
 
5.4%
p 5510
 
5.3%
l 5062
 
4.8%
s 5042
 
4.8%
e 5025
 
4.8%
o 4062
 
3.9%
m 4000
 
3.8%
Other values (29) 50423
48.3%
Distinct766
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:22.232120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length133
Median length132
Mean length107.394
Min length102

Characters and Unicode

Total characters107394
Distinct characters40
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique644 ?
Unique (%)64.4%

Sample

1st rowhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_x_medium
2nd rowhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_x_medium
3rd rowhttps://a0.muscache.com/im/pictures/user/fea78163-5495-401a-a620-ed948f59ac91.jpg?aki_policy=profile_x_medium
4th rowhttps://a0.muscache.com/im/pictures/user/67b13cea-8c11-49c0-a08d-7f42c330676e.jpg?aki_policy=profile_x_medium
5th rowhttps://a0.muscache.com/im/users/2649464/profile_pic/1339814302/original.jpg?aki_policy=profile_x_medium
ValueCountFrequency (%)
https://a0.muscache.com/im/pictures/user/6c5b1dec-21b3-4fdf-abc8-787d2fa9bde4.jpg?aki_policy=profile_x_medium 23
 
2.3%
https://a0.muscache.com/im/users/9270905/profile_pic/1381110921/original.jpg?aki_policy=profile_x_medium 12
 
1.2%
https://a0.muscache.com/im/users/792218/profile_pic/1310233853/original.jpg?aki_policy=profile_x_medium 9
 
0.9%
https://a0.muscache.com/im/users/5165851/profile_pic/1362412775/original.jpg?aki_policy=profile_x_medium 7
 
0.7%
https://a0.muscache.com/im/pictures/user/e7e2f4b9-91bd-42e0-91f4-c8497791b8c9.jpg?aki_policy=profile_x_medium 7
 
0.7%
https://a0.muscache.com/im/pictures/user/b6ae525a-fd1d-451f-922e-8ee6dfa2a16f.jpg?aki_policy=profile_x_medium 6
 
0.6%
https://a0.muscache.com/im/pictures/user/1d3acad1-e823-4577-993f-b953a4c514e5.jpg?aki_policy=profile_x_medium 5
 
0.5%
https://a0.muscache.com/im/pictures/user/d9351e88-fd2a-42b7-9c7a-1eb3caffd888.jpg?aki_policy=profile_x_medium 5
 
0.5%
https://a0.muscache.com/im/pictures/user/7e48ab44-bcf9-4f50-aae5-dfdf852b0932.jpg?aki_policy=profile_x_medium 5
 
0.5%
https://a0.muscache.com/im/users/1648634/profile_pic/1327454133/original.jpg?aki_policy=profile_x_medium 5
 
0.5%
Other values (756) 916
91.6%
2023-10-23T21:03:23.775741image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 7614
 
7.1%
/ 7104
 
6.6%
e 6025
 
5.6%
c 5924
 
5.5%
p 5510
 
5.1%
m 5000
 
4.7%
a 4628
 
4.3%
o 4062
 
3.8%
s 4042
 
3.8%
r 3594
 
3.3%
Other values (30) 53891
50.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 69080
64.3%
Decimal Number 19656
 
18.3%
Other Punctuation 12104
 
11.3%
Connector Punctuation 3510
 
3.3%
Dash Punctuation 2002
 
1.9%
Math Symbol 1000
 
0.9%
Uppercase Letter 42
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 7614
 
11.0%
e 6025
 
8.7%
c 5924
 
8.6%
p 5510
 
8.0%
m 5000
 
7.2%
a 4628
 
6.7%
o 4062
 
5.9%
s 4042
 
5.9%
r 3594
 
5.2%
u 3490
 
5.1%
Other values (12) 19191
27.8%
Decimal Number
ValueCountFrequency (%)
0 2637
13.4%
1 2351
12.0%
4 2212
11.3%
3 2038
10.4%
8 1848
9.4%
9 1767
9.0%
7 1732
8.8%
5 1717
8.7%
2 1710
8.7%
6 1644
8.4%
Other Punctuation
ValueCountFrequency (%)
/ 7104
58.7%
. 3000
24.8%
? 1000
 
8.3%
: 1000
 
8.3%
Connector Punctuation
ValueCountFrequency (%)
_ 3510
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2002
100.0%
Math Symbol
ValueCountFrequency (%)
= 1000
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 69122
64.4%
Common 38272
35.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 7614
 
11.0%
e 6025
 
8.7%
c 5924
 
8.6%
p 5510
 
8.0%
m 5000
 
7.2%
a 4628
 
6.7%
o 4062
 
5.9%
s 4042
 
5.8%
r 3594
 
5.2%
u 3490
 
5.0%
Other values (13) 19233
27.8%
Common
ValueCountFrequency (%)
/ 7104
18.6%
_ 3510
 
9.2%
. 3000
 
7.8%
0 2637
 
6.9%
1 2351
 
6.1%
4 2212
 
5.8%
3 2038
 
5.3%
- 2002
 
5.2%
8 1848
 
4.8%
9 1767
 
4.6%
Other values (7) 9803
25.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 7614
 
7.1%
/ 7104
 
6.6%
e 6025
 
5.6%
c 5924
 
5.5%
p 5510
 
5.1%
m 5000
 
4.7%
a 4628
 
4.3%
o 4062
 
3.8%
s 4042
 
3.8%
r 3594
 
3.3%
Other values (30) 53891
50.2%
Distinct49
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:24.450011image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length30
Median length24
Mean length8.806
Min length3

Characters and Unicode

Total characters8806
Distinct characters48
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)1.9%

Sample

1st rowCopacabana
2nd rowCopacabana
3rd rowCopacabana
4th rowCopacabana
5th rowCentro
ValueCountFrequency (%)
copacabana 328
27.2%
no_info 153
12.7%
ipanema 133
11.0%
tijuca 59
 
4.9%
santa 57
 
4.7%
teresa 57
 
4.7%
da 56
 
4.6%
barra 55
 
4.6%
leblon 54
 
4.5%
botafogo 42
 
3.5%
Other values (54) 212
17.6%
2023-10-23T21:03:25.805845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2224
25.3%
n 956
10.9%
o 913
10.4%
p 484
 
5.5%
e 447
 
5.1%
c 418
 
4.7%
b 385
 
4.4%
C 351
 
4.0%
i 292
 
3.3%
r 246
 
2.8%
Other values (38) 2090
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7457
84.7%
Uppercase Letter 990
 
11.2%
Space Separator 206
 
2.3%
Connector Punctuation 153
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2224
29.8%
n 956
12.8%
o 913
12.2%
p 484
 
6.5%
e 447
 
6.0%
c 418
 
5.6%
b 385
 
5.2%
i 292
 
3.9%
r 246
 
3.3%
f 195
 
2.6%
Other values (18) 897
12.0%
Uppercase Letter
ValueCountFrequency (%)
C 351
35.5%
I 136
 
13.7%
L 120
 
12.1%
T 117
 
11.8%
B 111
 
11.2%
S 65
 
6.6%
G 19
 
1.9%
F 17
 
1.7%
V 11
 
1.1%
P 8
 
0.8%
Other values (8) 35
 
3.5%
Space Separator
ValueCountFrequency (%)
206
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 153
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8447
95.9%
Common 359
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2224
26.3%
n 956
11.3%
o 913
10.8%
p 484
 
5.7%
e 447
 
5.3%
c 418
 
4.9%
b 385
 
4.6%
C 351
 
4.2%
i 292
 
3.5%
r 246
 
2.9%
Other values (36) 1731
20.5%
Common
ValueCountFrequency (%)
206
57.4%
_ 153
42.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8762
99.5%
None 44
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2224
25.4%
n 956
10.9%
o 913
10.4%
p 484
 
5.5%
e 447
 
5.1%
c 418
 
4.8%
b 385
 
4.4%
C 351
 
4.0%
i 292
 
3.3%
r 246
 
2.8%
Other values (31) 2046
23.4%
None
ValueCountFrequency (%)
á 21
47.7%
ã 7
 
15.9%
â 6
 
13.6%
ó 6
 
13.6%
ç 2
 
4.5%
ê 1
 
2.3%
í 1
 
2.3%

host_listings_count
Real number (ℝ)

Distinct31
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.047
Minimum1
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:26.713434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile33
Maximum123
Range122
Interquartile range (IQR)4

Descriptive statistics

Standard deviation12.90935484
Coefficient of variation (CV)2.134836256
Kurtosis27.91657398
Mean6.047
Median Absolute Deviation (MAD)1
Skewness4.747475256
Sum6047
Variance166.6514424
MonotonicityNot monotonic
2023-10-23T21:03:27.486817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 383
38.3%
2 196
19.6%
3 88
 
8.8%
4 74
 
7.4%
5 39
 
3.9%
6 32
 
3.2%
8 32
 
3.2%
7 31
 
3.1%
10 23
 
2.3%
56 23
 
2.3%
Other values (21) 79
 
7.9%
ValueCountFrequency (%)
1 383
38.3%
2 196
19.6%
3 88
 
8.8%
4 74
 
7.4%
5 39
 
3.9%
ValueCountFrequency (%)
123 3
 
0.3%
81 3
 
0.3%
57 4
 
0.4%
56 23
2.3%
48 5
 
0.5%

host_total_listings_count
Real number (ℝ)

Distinct45
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.491
Minimum1
Maximum399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:28.307746image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q38
95-th percentile69
Maximum399
Range398
Interquartile range (IQR)6

Descriptive statistics

Standard deviation33.66492605
Coefficient of variation (CV)2.695134581
Kurtosis65.61054703
Mean12.491
Median Absolute Deviation (MAD)3
Skewness7.086007642
Sum12491
Variance1133.327246
MonotonicityNot monotonic
2023-10-23T21:03:29.063466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1 203
20.3%
2 164
16.4%
3 122
12.2%
4 89
8.9%
5 48
 
4.8%
6 47
 
4.7%
7 44
 
4.4%
8 35
 
3.5%
9 35
 
3.5%
86 23
 
2.3%
Other values (35) 190
19.0%
ValueCountFrequency (%)
1 203
20.3%
2 164
16.4%
3 122
12.2%
4 89
8.9%
5 48
 
4.8%
ValueCountFrequency (%)
399 3
 
0.3%
288 3
 
0.3%
130 3
 
0.3%
122 1
 
0.1%
119 12
1.2%
Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:29.528092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length32
Median length18
Mean length18.524
Min length9

Characters and Unicode

Total characters18524
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row['email', 'phone']
2nd row['email', 'phone']
3rd row['email', 'phone']
4th row['email', 'phone']
5th row['email', 'phone']
ValueCountFrequency (%)
phone 995
49.5%
email 931
46.3%
work_email 85
 
4.2%
2023-10-23T21:03:30.701411image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 4022
21.7%
e 2011
10.9%
o 1080
 
5.8%
m 1016
 
5.5%
a 1016
 
5.5%
i 1016
 
5.5%
l 1016
 
5.5%
, 1011
 
5.5%
1011
 
5.5%
[ 1000
 
5.4%
Other values (8) 4325
23.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10395
56.1%
Other Punctuation 5033
27.2%
Space Separator 1011
 
5.5%
Open Punctuation 1000
 
5.4%
Close Punctuation 1000
 
5.4%
Connector Punctuation 85
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2011
19.3%
o 1080
10.4%
m 1016
9.8%
a 1016
9.8%
i 1016
9.8%
l 1016
9.8%
n 995
9.6%
p 995
9.6%
h 995
9.6%
w 85
 
0.8%
Other values (2) 170
 
1.6%
Other Punctuation
ValueCountFrequency (%)
' 4022
79.9%
, 1011
 
20.1%
Space Separator
ValueCountFrequency (%)
1011
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 1000
100.0%
Close Punctuation
ValueCountFrequency (%)
] 1000
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 85
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10395
56.1%
Common 8129
43.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2011
19.3%
o 1080
10.4%
m 1016
9.8%
a 1016
9.8%
i 1016
9.8%
l 1016
9.8%
n 995
9.6%
p 995
9.6%
h 995
9.6%
w 85
 
0.8%
Other values (2) 170
 
1.6%
Common
ValueCountFrequency (%)
' 4022
49.5%
, 1011
 
12.4%
1011
 
12.4%
[ 1000
 
12.3%
] 1000
 
12.3%
_ 85
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18524
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 4022
21.7%
e 2011
10.9%
o 1080
 
5.8%
m 1016
 
5.5%
a 1016
 
5.5%
i 1016
 
5.5%
l 1016
 
5.5%
, 1011
 
5.5%
1011
 
5.5%
[ 1000
 
5.4%
Other values (8) 4325
23.3%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
848 
False
152 
ValueCountFrequency (%)
True 848
84.8%
False 152
 
15.2%
2023-10-23T21:03:31.291165image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct27
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:31.873531image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length52
Median length51
Mean length16.354
Min length7

Characters and Unicode

Total characters16354
Distinct characters39
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)2.0%

Sample

1st rowno_info
2nd rowno_info
3rd rowRio de Janeiro, Brazil
4th rowRio de Janeiro, Brazil
5th rowno_info
ValueCountFrequency (%)
rio 648
22.9%
de 565
20.0%
janeiro 564
19.9%
brazil 558
19.7%
no_info 442
15.6%
copacabana 19
 
0.7%
6
 
0.2%
ipanema 5
 
0.2%
da 2
 
0.1%
tijuca 2
 
0.1%
Other values (17) 21
 
0.7%
2023-10-23T21:03:33.616801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 2219
13.6%
o 2121
13.0%
1833
11.2%
n 1482
9.1%
a 1240
 
7.6%
e 1147
 
7.0%
r 1136
 
6.9%
, 685
 
4.2%
R 652
 
4.0%
d 568
 
3.5%
Other values (29) 3271
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11576
70.8%
Space Separator 1833
 
11.2%
Uppercase Letter 1816
 
11.1%
Other Punctuation 686
 
4.2%
Connector Punctuation 442
 
2.7%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2219
19.2%
o 2121
18.3%
n 1482
12.8%
a 1240
10.7%
e 1147
9.9%
r 1136
9.8%
d 568
 
4.9%
l 559
 
4.8%
z 558
 
4.8%
f 442
 
3.8%
Other values (12) 104
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
R 652
35.9%
J 566
31.2%
B 560
30.8%
C 19
 
1.0%
I 6
 
0.3%
T 4
 
0.2%
S 3
 
0.2%
L 2
 
0.1%
É 1
 
0.1%
D 1
 
0.1%
Other values (2) 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
, 685
99.9%
/ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
1833
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 442
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13392
81.9%
Common 2962
 
18.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2219
16.6%
o 2121
15.8%
n 1482
11.1%
a 1240
9.3%
e 1147
8.6%
r 1136
8.5%
R 652
 
4.9%
d 568
 
4.2%
J 566
 
4.2%
B 560
 
4.2%
Other values (24) 1701
12.7%
Common
ValueCountFrequency (%)
1833
61.9%
, 685
 
23.1%
_ 442
 
14.9%
- 1
 
< 0.1%
/ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16351
> 99.9%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 2219
13.6%
o 2121
13.0%
1833
11.2%
n 1482
9.1%
a 1240
 
7.6%
e 1147
 
7.0%
r 1136
 
6.9%
, 685
 
4.2%
R 652
 
4.0%
d 568
 
3.5%
Other values (26) 3268
20.0%
None
ValueCountFrequency (%)
É 1
33.3%
ç 1
33.3%
á 1
33.3%
Distinct53
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:34.232671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length24
Median length18
Mean length9.293
Min length3

Characters and Unicode

Total characters9293
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)1.6%

Sample

1st rowCopacabana
2nd rowCopacabana
3rd rowCopacabana
4th rowCopacabana
5th rowCentro
ValueCountFrequency (%)
copacabana 377
30.0%
ipanema 141
 
11.2%
santa 68
 
5.4%
teresa 68
 
5.4%
tijuca 67
 
5.3%
da 64
 
5.1%
leblon 62
 
4.9%
barra 60
 
4.8%
botafogo 50
 
4.0%
leme 23
 
1.8%
Other values (60) 277
22.0%
2023-10-23T21:03:35.630361image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2600
28.0%
n 768
 
8.3%
o 736
 
7.9%
e 542
 
5.8%
p 532
 
5.7%
c 503
 
5.4%
b 443
 
4.8%
C 420
 
4.5%
r 348
 
3.7%
257
 
2.8%
Other values (37) 2144
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7853
84.5%
Uppercase Letter 1183
 
12.7%
Space Separator 257
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2600
33.1%
n 768
 
9.8%
o 736
 
9.4%
e 542
 
6.9%
p 532
 
6.8%
c 503
 
6.4%
b 443
 
5.6%
r 348
 
4.4%
m 212
 
2.7%
t 183
 
2.3%
Other values (18) 986
 
12.6%
Uppercase Letter
ValueCountFrequency (%)
C 420
35.5%
I 145
 
12.3%
T 141
 
11.9%
B 137
 
11.6%
L 114
 
9.6%
S 81
 
6.8%
G 28
 
2.4%
J 27
 
2.3%
F 18
 
1.5%
V 18
 
1.5%
Other values (8) 54
 
4.6%
Space Separator
ValueCountFrequency (%)
257
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9036
97.2%
Common 257
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2600
28.8%
n 768
 
8.5%
o 736
 
8.1%
e 542
 
6.0%
p 532
 
5.9%
c 503
 
5.6%
b 443
 
4.9%
C 420
 
4.6%
r 348
 
3.9%
m 212
 
2.3%
Other values (36) 1932
21.4%
Common
ValueCountFrequency (%)
257
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9213
99.1%
None 80
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2600
28.2%
n 768
 
8.3%
o 736
 
8.0%
e 542
 
5.9%
p 532
 
5.8%
c 503
 
5.5%
b 443
 
4.8%
C 420
 
4.6%
r 348
 
3.8%
257
 
2.8%
Other values (30) 2064
22.4%
None
ValueCountFrequency (%)
á 42
52.5%
ã 12
 
15.0%
â 11
 
13.8%
ó 9
 
11.2%
ç 3
 
3.8%
ú 2
 
2.5%
í 1
 
1.2%

latitude
Real number (ℝ)

Distinct902
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.96671819
Minimum-23.0662
Maximum-22.7909
Zeros0
Zeros (%)0.0%
Negative1000
Negative (%)100.0%
Memory size7.9 KiB
2023-10-23T21:03:36.244863image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-23.0662
5-th percentile-23.0070025
Q1-22.98377
median-22.974975
Q3-22.95679
95-th percentile-22.915209
Maximum-22.7909
Range0.2753
Interquartile range (IQR)0.02698

Descriptive statistics

Standard deviation0.02816403148
Coefficient of variation (CV)-0.001226297603
Kurtosis2.936672504
Mean-22.96671819
Median Absolute Deviation (MAD)0.010285
Skewness1.235196579
Sum-22966.71819
Variance0.0007932126689
MonotonicityNot monotonic
2023-10-23T21:03:36.977455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22.97995 4
 
0.4%
-22.98069 3
 
0.3%
-22.91814 3
 
0.3%
-22.98394 3
 
0.3%
-22.98583 3
 
0.3%
-22.98174 3
 
0.3%
-22.92119 3
 
0.3%
-22.98251 3
 
0.3%
-22.9791 3
 
0.3%
-22.97531 3
 
0.3%
Other values (892) 969
96.9%
ValueCountFrequency (%)
-23.0662 1
0.1%
-23.03154 1
0.1%
-23.0231 1
0.1%
-23.02219 1
0.1%
-23.02158 1
0.1%
ValueCountFrequency (%)
-22.7909 1
0.1%
-22.84008 1
0.1%
-22.84087 1
0.1%
-22.84124 1
0.1%
-22.84208 1
0.1%

longitude
Real number (ℝ)

Distinct909
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-43.21432943
Minimum-43.61203
Maximum-43.16125
Zeros0
Zeros (%)0.0%
Negative1000
Negative (%)100.0%
Memory size7.9 KiB
2023-10-23T21:03:37.674525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-43.61203
5-th percentile-43.365412
Q1-43.209305
median-43.191859
Q3-43.185075
95-th percentile-43.174879
Maximum-43.16125
Range0.45078
Interquartile range (IQR)0.02423

Descriptive statistics

Standard deviation0.06204194403
Coefficient of variation (CV)-0.001435679897
Kurtosis8.02342453
Mean-43.21432943
Median Absolute Deviation (MAD)0.009545
Skewness-2.781354868
Sum-43214.32943
Variance0.003849202819
MonotonicityNot monotonic
2023-10-23T21:03:38.338573image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-43.19024 5
 
0.5%
-43.19215 4
 
0.4%
-43.19054 4
 
0.4%
-43.17754 3
 
0.3%
-43.19277 3
 
0.3%
-43.18935 3
 
0.3%
-43.19109 3
 
0.3%
-43.19146 3
 
0.3%
-43.18971 3
 
0.3%
-43.18539 3
 
0.3%
Other values (899) 966
96.6%
ValueCountFrequency (%)
-43.61203 1
0.1%
-43.5659 1
0.1%
-43.51179 1
0.1%
-43.49922 1
0.1%
-43.49149 1
0.1%
ValueCountFrequency (%)
-43.16125 1
0.1%
-43.16144 1
0.1%
-43.16163 1
0.1%
-43.16211 1
0.1%
-43.16236 1
0.1%
Distinct29
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:38.879257image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length34
Median length18
Mean length18.948
Min length4

Characters and Unicode

Total characters18948
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)1.1%

Sample

1st rowEntire rental unit
2nd rowEntire rental unit
3rd rowEntire rental unit
4th rowEntire condo
5th rowEntire rental unit
ValueCountFrequency (%)
unit 778
23.8%
rental 778
23.8%
entire 762
23.3%
room 233
 
7.1%
in 233
 
7.1%
private 228
 
7.0%
home 93
 
2.8%
condo 60
 
1.8%
loft 28
 
0.9%
serviced 13
 
0.4%
Other values (18) 64
 
2.0%
2023-10-23T21:03:40.072675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 2638
13.9%
n 2636
13.9%
2270
12.0%
r 2041
10.8%
i 2021
10.7%
e 1957
10.3%
a 1059
5.6%
l 813
 
4.3%
u 807
 
4.3%
E 765
 
4.0%
Other values (19) 1941
10.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15678
82.7%
Space Separator 2270
 
12.0%
Uppercase Letter 1000
 
5.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 2638
16.8%
n 2636
16.8%
r 2041
13.0%
i 2021
12.9%
e 1957
12.5%
a 1059
6.8%
l 813
 
5.2%
u 807
 
5.1%
o 726
 
4.6%
m 339
 
2.2%
Other values (12) 641
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
E 765
76.5%
P 228
 
22.8%
S 4
 
0.4%
T 1
 
0.1%
B 1
 
0.1%
R 1
 
0.1%
Space Separator
ValueCountFrequency (%)
2270
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16678
88.0%
Common 2270
 
12.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 2638
15.8%
n 2636
15.8%
r 2041
12.2%
i 2021
12.1%
e 1957
11.7%
a 1059
6.3%
l 813
 
4.9%
u 807
 
4.8%
E 765
 
4.6%
o 726
 
4.4%
Other values (18) 1215
7.3%
Common
ValueCountFrequency (%)
2270
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 2638
13.9%
n 2636
13.9%
2270
12.0%
r 2041
10.8%
i 2021
10.7%
e 1957
10.3%
a 1059
5.6%
l 813
 
4.3%
u 807
 
4.3%
E 765
 
4.0%
Other values (19) 1941
10.2%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:40.491910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length15
Median length15
Mean length14.297
Min length11

Characters and Unicode

Total characters14297
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntire home/apt
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowEntire home/apt
ValueCountFrequency (%)
entire 767
38.4%
home/apt 767
38.4%
room 233
 
11.7%
private 229
 
11.5%
shared 4
 
0.2%
2023-10-23T21:03:41.922890image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1767
12.4%
t 1763
12.3%
o 1233
8.6%
r 1233
8.6%
a 1000
 
7.0%
1000
 
7.0%
m 1000
 
7.0%
i 996
 
7.0%
h 771
 
5.4%
p 767
 
5.4%
Other values (7) 2767
19.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11530
80.6%
Space Separator 1000
 
7.0%
Uppercase Letter 1000
 
7.0%
Other Punctuation 767
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1767
15.3%
t 1763
15.3%
o 1233
10.7%
r 1233
10.7%
a 1000
8.7%
m 1000
8.7%
i 996
8.6%
h 771
6.7%
p 767
6.7%
n 767
6.7%
Other values (2) 233
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
E 767
76.7%
P 229
 
22.9%
S 4
 
0.4%
Space Separator
ValueCountFrequency (%)
1000
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 767
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12530
87.6%
Common 1767
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1767
14.1%
t 1763
14.1%
o 1233
9.8%
r 1233
9.8%
a 1000
8.0%
m 1000
8.0%
i 996
7.9%
h 771
6.2%
p 767
6.1%
E 767
6.1%
Other values (5) 1233
9.8%
Common
ValueCountFrequency (%)
1000
56.6%
/ 767
43.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1767
12.4%
t 1763
12.3%
o 1233
8.6%
r 1233
8.6%
a 1000
 
7.0%
1000
 
7.0%
m 1000
 
7.0%
i 996
 
7.0%
h 771
 
5.4%
p 767
 
5.4%
Other values (7) 2767
19.4%

accommodates
Real number (ℝ)

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.039
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:42.602967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median4
Q35
95-th percentile8
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.412737097
Coefficient of variation (CV)0.5973600142
Kurtosis4.983485722
Mean4.039
Median Absolute Deviation (MAD)2
Skewness1.7927761
Sum4039
Variance5.8213003
MonotonicityNot monotonic
2023-10-23T21:03:43.471078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 276
27.6%
4 261
26.1%
3 124
12.4%
6 117
11.7%
5 67
 
6.7%
1 49
 
4.9%
8 45
 
4.5%
10 17
 
1.7%
7 17
 
1.7%
9 8
 
0.8%
Other values (5) 19
 
1.9%
ValueCountFrequency (%)
1 49
 
4.9%
2 276
27.6%
3 124
12.4%
4 261
26.1%
5 67
 
6.7%
ValueCountFrequency (%)
16 8
0.8%
14 1
 
0.1%
13 3
 
0.3%
12 6
0.6%
11 1
 
0.1%
Distinct27
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:44.162858image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length16
Median length14
Mean length7.754
Min length6

Characters and Unicode

Total characters7754
Distinct characters29
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.6%

Sample

1st row1 bath
2nd rowno_info
3rd row1 bath
4th row1 bath
5th row1 bath
ValueCountFrequency (%)
1 542
25.6%
bath 542
25.6%
baths 438
20.7%
2 198
 
9.4%
1.5 91
 
4.3%
shared 91
 
4.3%
3 62
 
2.9%
private 45
 
2.1%
2.5 40
 
1.9%
no_info 17
 
0.8%
Other values (12) 50
 
2.4%
2023-10-23T21:03:45.418174image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1122
14.5%
1116
14.4%
h 1077
13.9%
t 1028
13.3%
b 983
12.7%
1 634
8.2%
s 526
6.8%
2 238
 
3.1%
5 152
 
2.0%
. 146
 
1.9%
Other values (19) 732
9.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5342
68.9%
Decimal Number 1127
 
14.5%
Space Separator 1116
 
14.4%
Other Punctuation 146
 
1.9%
Connector Punctuation 17
 
0.2%
Uppercase Letter 3
 
< 0.1%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1122
21.0%
h 1077
20.2%
t 1028
19.2%
b 983
18.4%
s 526
9.8%
r 136
 
2.5%
e 136
 
2.5%
d 91
 
1.7%
i 62
 
1.2%
v 45
 
0.8%
Other values (5) 136
 
2.5%
Decimal Number
ValueCountFrequency (%)
1 634
56.3%
2 238
 
21.1%
5 152
 
13.5%
3 71
 
6.3%
4 18
 
1.6%
0 5
 
0.4%
7 4
 
0.4%
6 3
 
0.3%
8 2
 
0.2%
Space Separator
ValueCountFrequency (%)
1116
100.0%
Other Punctuation
ValueCountFrequency (%)
. 146
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 17
100.0%
Uppercase Letter
ValueCountFrequency (%)
S 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5345
68.9%
Common 2409
31.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1122
21.0%
h 1077
20.1%
t 1028
19.2%
b 983
18.4%
s 526
9.8%
r 136
 
2.5%
e 136
 
2.5%
d 91
 
1.7%
i 62
 
1.2%
v 45
 
0.8%
Other values (6) 139
 
2.6%
Common
ValueCountFrequency (%)
1116
46.3%
1 634
26.3%
2 238
 
9.9%
5 152
 
6.3%
. 146
 
6.1%
3 71
 
2.9%
4 18
 
0.7%
_ 17
 
0.7%
0 5
 
0.2%
7 4
 
0.2%
Other values (3) 8
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1122
14.5%
1116
14.4%
h 1077
13.9%
t 1028
13.3%
b 983
12.7%
1 634
8.2%
s 526
6.8%
2 238
 
3.1%
5 152
 
2.0%
. 146
 
1.9%
Other values (19) 732
9.4%

bedrooms
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.338
Minimum0
Maximum7
Zeros260
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:46.156828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.194647723
Coefficient of variation (CV)0.8928607799
Kurtosis1.821825334
Mean1.338
Median Absolute Deviation (MAD)1
Skewness1.104595868
Sum1338
Variance1.427183183
MonotonicityNot monotonic
2023-10-23T21:03:46.986982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 375
37.5%
0 260
26.0%
2 202
20.2%
3 117
 
11.7%
4 33
 
3.3%
6 5
 
0.5%
5 5
 
0.5%
7 3
 
0.3%
ValueCountFrequency (%)
0 260
26.0%
1 375
37.5%
2 202
20.2%
3 117
 
11.7%
4 33
 
3.3%
ValueCountFrequency (%)
7 3
 
0.3%
6 5
 
0.5%
5 5
 
0.5%
4 33
 
3.3%
3 117
11.7%

beds
Real number (ℝ)

Distinct18
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.653
Minimum0
Maximum50
Zeros10
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:47.651006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum50
Range50
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.531600284
Coefficient of variation (CV)0.9542405897
Kurtosis125.5375353
Mean2.653
Median Absolute Deviation (MAD)1
Skewness7.822878236
Sum2653
Variance6.409
MonotonicityNot monotonic
2023-10-23T21:03:48.569674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 326
32.6%
2 294
29.4%
3 135
13.5%
4 106
 
10.6%
5 52
 
5.2%
6 26
 
2.6%
7 19
 
1.9%
8 13
 
1.3%
0 10
 
1.0%
9 8
 
0.8%
Other values (8) 11
 
1.1%
ValueCountFrequency (%)
0 10
 
1.0%
1 326
32.6%
2 294
29.4%
3 135
13.5%
4 106
 
10.6%
ValueCountFrequency (%)
50 1
0.1%
17 1
0.1%
16 2
0.2%
15 1
0.1%
14 1
0.1%
Distinct956
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:50.233327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length1764
Median length705
Mean length474.323
Min length2

Characters and Unicode

Total characters474323
Distinct characters73
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique938 ?
Unique (%)93.8%

Sample

1st row["Hangers", "Body soap", "Elevator", "Bed linens", "Microwave", "Wifi", "Dishes and silverware", "Hair dryer", "TV", "Iron", "Dining table", "Ceiling fan", "Essentials", "Public or shared beach access \u2013 Beachfront", "Refrigerator", "Coffee maker: drip coffee maker", "Gas stove", "Hot water", "Extra pillows and blankets", "Kitchen", "Air conditioning"]
2nd row["TV", "Kitchen", "Wifi", "Elevator", "Air conditioning"]
3rd row["Clothing storage: wardrobe", "Public or shared beach access", "Hangers", "Esmaltec gas stove", "Elevator", "Cooking basics", "32\" HDTV", "Room-darkening shades", "Bed linens", "Microwave", "Free washer \u2013 In unit", "Drying rack for clothing", "Dishes and silverware", "Cleaning products", "Courtyard view", "Coffee maker", "Iron", "Laundromat nearby", "Dining table", "Ceiling fan", "Blender", "Essentials", "Hot water kettle", "Refrigerator", "Host greets you", "EV charger", "Mountain view", "Hot water", "Oven", "Kitchen", "Window AC unit", "Wifi \u2013 14 Mbps"]
4th row["Patio or balcony", "Hangers", "Paid parking off premises", "Elevator", "Cooking basics", "Private entrance", "Bed linens", "Microwave", "Wifi", "Dishes and silverware", "Hair dryer", "Self check-in", "Iron", "Building staff", "Essentials", "TV with standard cable", "Refrigerator", "Stove", "Smoking allowed", "Bathtub", "Hot water", "Oven", "Air conditioning", "Kitchen", "Luggage dropoff allowed", "Coffee maker"]
5th row["Hangers", "TV", "Kitchen", "Wifi", "Elevator"]
ValueCountFrequency (%)
and 1366
 
2.4%
allowed 1307
 
2.3%
wifi 990
 
1.7%
kitchen 934
 
1.6%
hot 927
 
1.6%
parking 898
 
1.6%
coffee 861
 
1.5%
water 847
 
1.5%
essentials 821
 
1.4%
free 777
 
1.4%
Other values (585) 47658
83.0%
2023-10-23T21:03:51.966885image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
56420
 
11.9%
" 52396
 
11.0%
e 37573
 
7.9%
a 28240
 
6.0%
r 26760
 
5.6%
, 25490
 
5.4%
i 25271
 
5.3%
o 22695
 
4.8%
n 21806
 
4.6%
s 20872
 
4.4%
Other values (63) 156800
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 302873
63.9%
Other Punctuation 79361
 
16.7%
Space Separator 56420
 
11.9%
Uppercase Letter 29165
 
6.1%
Decimal Number 3561
 
0.8%
Close Punctuation 1003
 
0.2%
Open Punctuation 1002
 
0.2%
Dash Punctuation 919
 
0.2%
Math Symbol 19
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 37573
12.4%
a 28240
 
9.3%
r 26760
 
8.8%
i 25271
 
8.3%
o 22695
 
7.5%
n 21806
 
7.2%
s 20872
 
6.9%
t 19492
 
6.4%
l 13540
 
4.5%
d 12275
 
4.1%
Other values (15) 74349
24.5%
Uppercase Letter
ValueCountFrequency (%)
C 3098
 
10.6%
H 2879
 
9.9%
B 2553
 
8.8%
E 2143
 
7.3%
D 2126
 
7.3%
S 2110
 
7.2%
W 1798
 
6.2%
P 1625
 
5.6%
F 1470
 
5.0%
L 1304
 
4.5%
Other values (14) 8059
27.6%
Decimal Number
ValueCountFrequency (%)
2 863
24.2%
0 844
23.7%
1 779
21.9%
3 677
19.0%
9 168
 
4.7%
4 115
 
3.2%
5 59
 
1.7%
6 23
 
0.6%
7 19
 
0.5%
8 14
 
0.4%
Other Punctuation
ValueCountFrequency (%)
" 52396
66.0%
, 25490
32.1%
\ 919
 
1.2%
: 471
 
0.6%
/ 66
 
0.1%
. 18
 
< 0.1%
' 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
[ 1000
99.8%
( 2
 
0.2%
Close Punctuation
ValueCountFrequency (%)
] 1000
99.7%
) 3
 
0.3%
Space Separator
ValueCountFrequency (%)
56420
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 919
100.0%
Math Symbol
ValueCountFrequency (%)
+ 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 332038
70.0%
Common 142285
30.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 37573
 
11.3%
a 28240
 
8.5%
r 26760
 
8.1%
i 25271
 
7.6%
o 22695
 
6.8%
n 21806
 
6.6%
s 20872
 
6.3%
t 19492
 
5.9%
l 13540
 
4.1%
d 12275
 
3.7%
Other values (39) 103514
31.2%
Common
ValueCountFrequency (%)
56420
39.7%
" 52396
36.8%
, 25490
17.9%
[ 1000
 
0.7%
] 1000
 
0.7%
\ 919
 
0.6%
- 919
 
0.6%
2 863
 
0.6%
0 844
 
0.6%
1 779
 
0.5%
Other values (14) 1655
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 474323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
56420
 
11.9%
" 52396
 
11.0%
e 37573
 
7.9%
a 28240
 
6.0%
r 26760
 
5.6%
, 25490
 
5.4%
i 25271
 
5.3%
o 22695
 
4.8%
n 21806
 
4.6%
s 20872
 
4.4%
Other values (63) 156800
33.1%

price
Text

Distinct432
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:52.685104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length10
Median length7
Mean length7.237
Min length6

Characters and Unicode

Total characters7237
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique254 ?
Unique (%)25.4%

Sample

1st row$180.00
2nd row$350.00
3rd row$190.00
4th row$279.00
5th row$300.00
ValueCountFrequency (%)
250.00 23
 
2.3%
300.00 23
 
2.3%
200.00 21
 
2.1%
150.00 17
 
1.7%
350.00 16
 
1.6%
130.00 16
 
1.6%
180.00 14
 
1.4%
500.00 13
 
1.3%
600.00 12
 
1.2%
220.00 11
 
1.1%
Other values (422) 834
83.4%
2023-10-23T21:03:54.056274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2660
36.8%
$ 1000
 
13.8%
. 1000
 
13.8%
1 462
 
6.4%
2 372
 
5.1%
3 329
 
4.5%
5 284
 
3.9%
4 257
 
3.6%
6 209
 
2.9%
9 194
 
2.7%
Other values (3) 470
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5113
70.7%
Other Punctuation 1124
 
15.5%
Currency Symbol 1000
 
13.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2660
52.0%
1 462
 
9.0%
2 372
 
7.3%
3 329
 
6.4%
5 284
 
5.6%
4 257
 
5.0%
6 209
 
4.1%
9 194
 
3.8%
7 190
 
3.7%
8 156
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 1000
89.0%
, 124
 
11.0%
Currency Symbol
ValueCountFrequency (%)
$ 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7237
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2660
36.8%
$ 1000
 
13.8%
. 1000
 
13.8%
1 462
 
6.4%
2 372
 
5.1%
3 329
 
4.5%
5 284
 
3.9%
4 257
 
3.6%
6 209
 
2.9%
9 194
 
2.7%
Other values (3) 470
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2660
36.8%
$ 1000
 
13.8%
. 1000
 
13.8%
1 462
 
6.4%
2 372
 
5.1%
3 329
 
4.5%
5 284
 
3.9%
4 257
 
3.6%
6 209
 
2.9%
9 194
 
2.7%
Other values (3) 470
 
6.5%

minimum_nights
Real number (ℝ)

Distinct26
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.237
Minimum1
Maximum630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:54.651951image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile10
Maximum630
Range629
Interquartile range (IQR)2

Descriptive statistics

Standard deviation26.93559647
Coefficient of variation (CV)4.318678287
Kurtosis333.8173647
Mean6.237
Median Absolute Deviation (MAD)1
Skewness16.5486981
Sum6237
Variance725.5263574
MonotonicityNot monotonic
2023-10-23T21:03:55.270464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
3 270
27.0%
2 257
25.7%
1 139
13.9%
4 121
12.1%
5 92
 
9.2%
7 41
 
4.1%
6 15
 
1.5%
90 13
 
1.3%
10 13
 
1.3%
30 7
 
0.7%
Other values (16) 32
 
3.2%
ValueCountFrequency (%)
1 139
13.9%
2 257
25.7%
3 270
27.0%
4 121
12.1%
5 92
 
9.2%
ValueCountFrequency (%)
630 1
 
0.1%
365 1
 
0.1%
300 1
 
0.1%
90 13
1.3%
60 2
 
0.2%

maximum_nights
Real number (ℝ)

Distinct70
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean502.931
Minimum3
Maximum1825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:55.946729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile15
Q159.75
median180
Q31125
95-th percentile1125
Maximum1825
Range1822
Interquartile range (IQR)1065.25

Descriptive statistics

Standard deviation502.1114978
Coefficient of variation (CV)0.9983705474
Kurtosis-1.73958918
Mean502.931
Median Absolute Deviation (MAD)170
Skewness0.3805457522
Sum502931
Variance252115.9562
MonotonicityNot monotonic
2023-10-23T21:03:56.702928image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1125 373
37.3%
90 134
 
13.4%
30 122
 
12.2%
365 57
 
5.7%
60 38
 
3.8%
180 35
 
3.5%
89 26
 
2.6%
15 25
 
2.5%
360 13
 
1.3%
28 12
 
1.2%
Other values (60) 165
16.5%
ValueCountFrequency (%)
3 1
 
0.1%
4 3
 
0.3%
5 1
 
0.1%
7 11
1.1%
8 2
 
0.2%
ValueCountFrequency (%)
1825 1
 
0.1%
1125 373
37.3%
1124 2
 
0.2%
1123 1
 
0.1%
1001 1
 
0.1%

minimum_minimum_nights
Real number (ℝ)

Distinct25
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.855
Minimum1
Maximum630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:57.289971image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile10
Maximum630
Range629
Interquartile range (IQR)2

Descriptive statistics

Standard deviation26.78319089
Coefficient of variation (CV)4.574413474
Kurtosis342.4149385
Mean5.855
Median Absolute Deviation (MAD)1
Skewness16.84233
Sum5855
Variance717.3393143
MonotonicityNot monotonic
2023-10-23T21:03:57.894847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2 275
27.5%
3 269
26.9%
1 172
17.2%
4 104
 
10.4%
5 76
 
7.6%
7 39
 
3.9%
90 12
 
1.2%
10 10
 
1.0%
6 9
 
0.9%
15 7
 
0.7%
Other values (15) 27
 
2.7%
ValueCountFrequency (%)
1 172
17.2%
2 275
27.5%
3 269
26.9%
4 104
 
10.4%
5 76
 
7.6%
ValueCountFrequency (%)
630 1
 
0.1%
365 1
 
0.1%
300 1
 
0.1%
90 12
1.2%
60 2
 
0.2%

maximum_minimum_nights
Real number (ℝ)

Distinct29
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.049
Minimum1
Maximum630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:58.483192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile14
Maximum630
Range629
Interquartile range (IQR)3

Descriptive statistics

Standard deviation27.0781799
Coefficient of variation (CV)3.841421464
Kurtosis324.9636322
Mean7.049
Median Absolute Deviation (MAD)2
Skewness16.24266741
Sum7049
Variance733.2278268
MonotonicityNot monotonic
2023-10-23T21:03:59.143394image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3 196
19.6%
2 193
19.3%
5 183
18.3%
1 119
11.9%
4 102
10.2%
7 86
8.6%
6 32
 
3.2%
10 22
 
2.2%
90 13
 
1.3%
30 9
 
0.9%
Other values (19) 45
 
4.5%
ValueCountFrequency (%)
1 119
11.9%
2 193
19.3%
3 196
19.6%
4 102
10.2%
5 183
18.3%
ValueCountFrequency (%)
630 1
 
0.1%
365 1
 
0.1%
300 1
 
0.1%
100 1
 
0.1%
90 13
1.3%

minimum_maximum_nights
Real number (ℝ)

Distinct67
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean607.302
Minimum1
Maximum1825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:03:59.835736image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q160
median365.5
Q31125
95-th percentile1125
Maximum1825
Range1824
Interquartile range (IQR)1065

Descriptive statistics

Standard deviation513.6673096
Coefficient of variation (CV)0.8458185706
Kurtosis-1.902483273
Mean607.302
Median Absolute Deviation (MAD)362
Skewness-0.0258618428
Sum607302
Variance263854.1049
MonotonicityNot monotonic
2023-10-23T21:04:00.617098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1125 477
47.7%
30 99
 
9.9%
90 99
 
9.9%
365 49
 
4.9%
60 30
 
3.0%
180 29
 
2.9%
15 23
 
2.3%
89 18
 
1.8%
360 11
 
1.1%
20 11
 
1.1%
Other values (57) 154
 
15.4%
ValueCountFrequency (%)
1 1
 
0.1%
2 4
0.4%
3 1
 
0.1%
4 5
0.5%
5 2
 
0.2%
ValueCountFrequency (%)
1825 1
 
0.1%
1125 477
47.7%
1124 2
 
0.2%
1123 1
 
0.1%
1000 2
 
0.2%

maximum_maximum_nights
Real number (ℝ)

Distinct66
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean640.685
Minimum3
Maximum1825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:04:01.310150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile15
Q189
median1125
Q31125
95-th percentile1125
Maximum1825
Range1822
Interquartile range (IQR)1036

Descriptive statistics

Standard deviation511.8497885
Coefficient of variation (CV)0.7989102109
Kurtosis-1.884837475
Mean640.685
Median Absolute Deviation (MAD)0
Skewness-0.1494329725
Sum640685
Variance261990.206
MonotonicityNot monotonic
2023-10-23T21:04:02.009693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1125 509
50.9%
30 97
 
9.7%
90 93
 
9.3%
365 49
 
4.9%
60 30
 
3.0%
180 30
 
3.0%
15 21
 
2.1%
89 18
 
1.8%
360 11
 
1.1%
28 9
 
0.9%
Other values (56) 133
 
13.3%
ValueCountFrequency (%)
3 1
 
0.1%
4 3
 
0.3%
5 1
 
0.1%
7 8
0.8%
8 2
 
0.2%
ValueCountFrequency (%)
1825 1
 
0.1%
1125 509
50.9%
1124 2
 
0.2%
1123 1
 
0.1%
1000 2
 
0.2%

minimum_nights_avg_ntm
Real number (ℝ)

Distinct73
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.307
Minimum1
Maximum630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:04:02.643841image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile10.195
Maximum630
Range629
Interquartile range (IQR)2

Descriptive statistics

Standard deviation26.92654917
Coefficient of variation (CV)4.269311744
Kurtosis334.0961384
Mean6.307
Median Absolute Deviation (MAD)1
Skewness16.55722069
Sum6307
Variance725.0390501
MonotonicityNot monotonic
2023-10-23T21:04:03.377144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 202
20.2%
2 192
19.2%
1 121
12.1%
4 91
9.1%
5 80
 
8.0%
7 37
 
3.7%
3.1 31
 
3.1%
3.2 26
 
2.6%
2.1 21
 
2.1%
2.2 19
 
1.9%
Other values (63) 180
18.0%
ValueCountFrequency (%)
1 121
12.1%
1.1 2
 
0.2%
1.3 4
 
0.4%
1.4 3
 
0.3%
1.5 1
 
0.1%
ValueCountFrequency (%)
630 1
 
0.1%
365 1
 
0.1%
300 1
 
0.1%
90 12
1.2%
89.8 1
 
0.1%

maximum_nights_avg_ntm
Real number (ℝ)

Distinct100
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean629.4606
Minimum3
Maximum1825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:04:04.066890image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile15
Q189
median867.35
Q31125
95-th percentile1125
Maximum1825
Range1822
Interquartile range (IQR)1036

Descriptive statistics

Standard deviation507.7154831
Coefficient of variation (CV)0.8065881853
Kurtosis-1.878084775
Mean629.4606
Median Absolute Deviation (MAD)257.65
Skewness-0.1077504502
Sum629460.6
Variance257775.0118
MonotonicityNot monotonic
2023-10-23T21:04:04.836256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1125 477
47.7%
30 97
 
9.7%
90 87
 
8.7%
365 49
 
4.9%
60 30
 
3.0%
180 29
 
2.9%
15 20
 
2.0%
89 19
 
1.9%
360 11
 
1.1%
20 9
 
0.9%
Other values (90) 172
 
17.2%
ValueCountFrequency (%)
3 1
 
0.1%
4 3
 
0.3%
5 1
 
0.1%
7 8
0.8%
8 2
 
0.2%
ValueCountFrequency (%)
1825 1
 
0.1%
1125 477
47.7%
1124 2
 
0.2%
1123 1
 
0.1%
1117.3 1
 
0.1%

calendar_updated
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1000
Missing (%)100.0%
Memory size7.9 KiB

has_availability
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
960 
False
 
40
ValueCountFrequency (%)
True 960
96.0%
False 40
 
4.0%
2023-10-23T21:04:05.411093image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

availability_30
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.703
Minimum0
Maximum30
Zeros227
Zeros (%)22.7%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:04:05.890760image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median13
Q328
95-th percentile30
Maximum30
Range30
Interquartile range (IQR)27

Descriptive statistics

Standard deviation12.07553765
Coefficient of variation (CV)0.8212975347
Kurtosis-1.67075775
Mean14.703
Median Absolute Deviation (MAD)13
Skewness0.05199463459
Sum14703
Variance145.8186096
MonotonicityNot monotonic
2023-10-23T21:04:06.547545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 227
22.7%
30 170
17.0%
29 62
 
6.2%
28 54
 
5.4%
6 31
 
3.1%
5 31
 
3.1%
27 31
 
3.1%
23 28
 
2.8%
1 27
 
2.7%
13 27
 
2.7%
Other values (21) 312
31.2%
ValueCountFrequency (%)
0 227
22.7%
1 27
 
2.7%
2 20
 
2.0%
3 15
 
1.5%
4 26
 
2.6%
ValueCountFrequency (%)
30 170
17.0%
29 62
 
6.2%
28 54
 
5.4%
27 31
 
3.1%
26 10
 
1.0%

availability_60
Real number (ℝ)

ZEROS 

Distinct61
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.255
Minimum0
Maximum60
Zeros185
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:04:07.215050image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median28
Q357
95-th percentile60
Maximum60
Range60
Interquartile range (IQR)50

Descriptive statistics

Standard deviation23.31067057
Coefficient of variation (CV)0.770473329
Kurtosis-1.588839388
Mean30.255
Median Absolute Deviation (MAD)26
Skewness0.01404278273
Sum30255
Variance543.3873624
MonotonicityNot monotonic
2023-10-23T21:04:07.942015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 185
 
18.5%
60 145
 
14.5%
58 52
 
5.2%
59 42
 
4.2%
57 23
 
2.3%
53 17
 
1.7%
12 16
 
1.6%
14 16
 
1.6%
7 15
 
1.5%
20 15
 
1.5%
Other values (51) 474
47.4%
ValueCountFrequency (%)
0 185
18.5%
1 13
 
1.3%
2 10
 
1.0%
3 10
 
1.0%
4 11
 
1.1%
ValueCountFrequency (%)
60 145
14.5%
59 42
 
4.2%
58 52
 
5.2%
57 23
 
2.3%
56 10
 
1.0%

availability_90
Real number (ℝ)

ZEROS 

Distinct91
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.879
Minimum0
Maximum90
Zeros164
Zeros (%)16.4%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:04:08.621209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median52
Q386.25
95-th percentile90
Maximum90
Range90
Interquartile range (IQR)69.25

Descriptive statistics

Standard deviation33.88756004
Coefficient of variation (CV)0.6793953376
Kurtosis-1.441912714
Mean49.879
Median Absolute Deviation (MAD)35
Skewness-0.2355872261
Sum49879
Variance1148.366726
MonotonicityNot monotonic
2023-10-23T21:04:09.332790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 164
 
16.4%
90 144
 
14.4%
88 44
 
4.4%
89 42
 
4.2%
87 20
 
2.0%
83 18
 
1.8%
38 16
 
1.6%
51 14
 
1.4%
55 14
 
1.4%
85 14
 
1.4%
Other values (81) 510
51.0%
ValueCountFrequency (%)
0 164
16.4%
1 9
 
0.9%
2 7
 
0.7%
3 6
 
0.6%
4 2
 
0.2%
ValueCountFrequency (%)
90 144
14.4%
89 42
 
4.2%
88 44
 
4.4%
87 20
 
2.0%
86 12
 
1.2%

availability_365
Real number (ℝ)

ZEROS 

Distinct311
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209.118
Minimum0
Maximum365
Zeros97
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:04:10.260276image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q187
median227
Q3344
95-th percentile365
Maximum365
Range365
Interquartile range (IQR)257

Descriptive statistics

Standard deviation131.4576327
Coefficient of variation (CV)0.6286289686
Kurtosis-1.42229112
Mean209.118
Median Absolute Deviation (MAD)124
Skewness-0.2666740126
Sum209118
Variance17281.10919
MonotonicityNot monotonic
2023-10-23T21:04:10.992010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 97
 
9.7%
365 87
 
8.7%
364 26
 
2.6%
363 21
 
2.1%
362 16
 
1.6%
346 10
 
1.0%
359 10
 
1.0%
360 8
 
0.8%
327 8
 
0.8%
354 8
 
0.8%
Other values (301) 709
70.9%
ValueCountFrequency (%)
0 97
9.7%
1 2
 
0.2%
2 2
 
0.2%
3 2
 
0.2%
4 1
 
0.1%
ValueCountFrequency (%)
365 87
8.7%
364 26
 
2.6%
363 21
 
2.1%
362 16
 
1.6%
361 7
 
0.7%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2023-09-22 00:00:00
Maximum2023-09-23 00:00:00
2023-10-23T21:04:11.557972image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-23T21:04:12.138169image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

number_of_reviews
Real number (ℝ)

ZEROS 

Distinct253
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.851
Minimum0
Maximum611
Zeros97
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:04:12.854291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median28
Q395
95-th percentile262.1
Maximum611
Range611
Interquartile range (IQR)89

Descriptive statistics

Standard deviation91.36577315
Coefficient of variation (CV)1.366707651
Kurtosis6.023196816
Mean66.851
Median Absolute Deviation (MAD)27
Skewness2.227924827
Sum66851
Variance8347.704504
MonotonicityNot monotonic
2023-10-23T21:04:13.709759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 97
 
9.7%
1 40
 
4.0%
5 29
 
2.9%
4 29
 
2.9%
2 27
 
2.7%
6 24
 
2.4%
3 24
 
2.4%
8 21
 
2.1%
20 18
 
1.8%
11 17
 
1.7%
Other values (243) 674
67.4%
ValueCountFrequency (%)
0 97
9.7%
1 40
4.0%
2 27
 
2.7%
3 24
 
2.4%
4 29
 
2.9%
ValueCountFrequency (%)
611 1
0.1%
577 1
0.1%
542 1
0.1%
540 1
0.1%
517 1
0.1%

number_of_reviews_ltm
Real number (ℝ)

ZEROS 

Distinct57
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.73
Minimum0
Maximum68
Zeros349
Zeros (%)34.9%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:04:14.620060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q314
95-th percentile35
Maximum68
Range68
Interquartile range (IQR)14

Descriptive statistics

Standard deviation12.22909034
Coefficient of variation (CV)1.400812181
Kurtosis2.827610998
Mean8.73
Median Absolute Deviation (MAD)3
Skewness1.748133143
Sum8730
Variance149.5506507
MonotonicityNot monotonic
2023-10-23T21:04:15.495321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 349
34.9%
1 84
 
8.4%
2 59
 
5.9%
4 41
 
4.1%
3 31
 
3.1%
6 30
 
3.0%
5 25
 
2.5%
14 23
 
2.3%
7 23
 
2.3%
9 22
 
2.2%
Other values (47) 313
31.3%
ValueCountFrequency (%)
0 349
34.9%
1 84
 
8.4%
2 59
 
5.9%
3 31
 
3.1%
4 41
 
4.1%
ValueCountFrequency (%)
68 1
0.1%
62 1
0.1%
61 1
0.1%
60 1
0.1%
58 1
0.1%

number_of_reviews_l30d
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.563
Minimum0
Maximum8
Zeros706
Zeros (%)70.6%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:04:16.269278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.107816888
Coefficient of variation (CV)1.967703177
Kurtosis8.561358941
Mean0.563
Median Absolute Deviation (MAD)0
Skewness2.629123115
Sum563
Variance1.227258258
MonotonicityNot monotonic
2023-10-23T21:04:16.965347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 706
70.6%
1 150
 
15.0%
2 74
 
7.4%
3 38
 
3.8%
4 20
 
2.0%
5 6
 
0.6%
6 3
 
0.3%
8 2
 
0.2%
7 1
 
0.1%
ValueCountFrequency (%)
0 706
70.6%
1 150
 
15.0%
2 74
 
7.4%
3 38
 
3.8%
4 20
 
2.0%
ValueCountFrequency (%)
8 2
 
0.2%
7 1
 
0.1%
6 3
 
0.3%
5 6
 
0.6%
4 20
2.0%

first_review
Date

MISSING 

Distinct566
Distinct (%)62.7%
Missing97
Missing (%)9.7%
Memory size7.9 KiB
Minimum2010-06-07 00:00:00
Maximum2023-07-02 00:00:00
2023-10-23T21:04:17.638272image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-23T21:04:18.255157image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

last_review
Date

MISSING 

Distinct369
Distinct (%)40.9%
Missing97
Missing (%)9.7%
Memory size7.9 KiB
Minimum2012-02-21 00:00:00
Maximum2023-09-22 00:00:00
2023-10-23T21:04:18.880599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-23T21:04:19.629906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

review_scores_rating
Real number (ℝ)

Distinct92
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.16022
Minimum-1
Maximum5
Zeros3
Zeros (%)0.3%
Negative97
Negative (%)9.7%
Memory size7.9 KiB
2023-10-23T21:04:20.280310image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q14.56
median4.77
Q34.89
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation1.73393116
Coefficient of variation (CV)0.4167883334
Kurtosis4.785506576
Mean4.16022
Median Absolute Deviation (MAD)0.15
Skewness-2.56675144
Sum4160.22
Variance3.006517269
MonotonicityNot monotonic
2023-10-23T21:04:20.992938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 115
 
11.5%
-1 97
 
9.7%
4.8 35
 
3.5%
4.67 27
 
2.7%
4.75 27
 
2.7%
4.86 25
 
2.5%
4.88 24
 
2.4%
4.5 23
 
2.3%
4.79 23
 
2.3%
4.71 21
 
2.1%
Other values (82) 583
58.3%
ValueCountFrequency (%)
-1 97
9.7%
0 3
 
0.3%
1 1
 
0.1%
2.5 1
 
0.1%
3 2
 
0.2%
ValueCountFrequency (%)
5 115
11.5%
4.99 1
 
0.1%
4.98 8
 
0.8%
4.97 12
 
1.2%
4.96 6
 
0.6%

review_scores_accuracy
Real number (ℝ)

Distinct89
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1903
Minimum-1
Maximum5
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)10.0%
Memory size7.9 KiB
2023-10-23T21:04:21.639770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q14.61
median4.81
Q34.92
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation1.751373352
Coefficient of variation (CV)0.4179589414
Kurtosis4.789630243
Mean4.1903
Median Absolute Deviation (MAD)0.13
Skewness-2.572348431
Sum4190.3
Variance3.067308619
MonotonicityNot monotonic
2023-10-23T21:04:22.321696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 140
 
14.0%
-1 100
 
10.0%
4.86 34
 
3.4%
4.93 32
 
3.2%
4.83 27
 
2.7%
4.85 26
 
2.6%
4.89 25
 
2.5%
4.91 25
 
2.5%
4.8 24
 
2.4%
4.88 24
 
2.4%
Other values (79) 543
54.3%
ValueCountFrequency (%)
-1 100
10.0%
1 1
 
0.1%
2.5 1
 
0.1%
3 1
 
0.1%
3.5 2
 
0.2%
ValueCountFrequency (%)
5 140
14.0%
4.99 5
 
0.5%
4.98 7
 
0.7%
4.97 11
 
1.1%
4.96 15
 
1.5%

review_scores_cleanliness
Real number (ℝ)

Distinct105
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.12098
Minimum-1
Maximum5
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)10.0%
Memory size7.9 KiB
2023-10-23T21:04:23.046009image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q14.47
median4.75
Q34.9
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation1.742054705
Coefficient of variation (CV)0.42272826
Kurtosis4.553755548
Mean4.12098
Median Absolute Deviation (MAD)0.19
Skewness-2.509449928
Sum4120.98
Variance3.034754594
MonotonicityNot monotonic
2023-10-23T21:04:23.823426image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 127
 
12.7%
-1 100
 
10.0%
4.8 27
 
2.7%
4.9 26
 
2.6%
4.67 26
 
2.6%
4.86 24
 
2.4%
4.89 23
 
2.3%
4.75 21
 
2.1%
4.78 20
 
2.0%
4.94 20
 
2.0%
Other values (95) 586
58.6%
ValueCountFrequency (%)
-1 100
10.0%
1 1
 
0.1%
2 4
 
0.4%
3 3
 
0.3%
3.4 1
 
0.1%
ValueCountFrequency (%)
5 127
12.7%
4.99 3
 
0.3%
4.98 9
 
0.9%
4.97 3
 
0.3%
4.96 12
 
1.2%

review_scores_checkin
Real number (ℝ)

Distinct69
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.27783
Minimum-1
Maximum5
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)10.0%
Memory size7.9 KiB
2023-10-23T21:04:24.555594image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q14.79
median4.91
Q34.98
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation1.773542964
Coefficient of variation (CV)0.4145893979
Kurtosis4.907499979
Mean4.27783
Median Absolute Deviation (MAD)0.09
Skewness-2.606216068
Sum4277.83
Variance3.145454646
MonotonicityNot monotonic
2023-10-23T21:04:25.300962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 223
22.3%
-1 100
 
10.0%
4.97 48
 
4.8%
4.92 37
 
3.7%
4.95 35
 
3.5%
4.91 35
 
3.5%
4.98 34
 
3.4%
4.9 34
 
3.4%
4.96 33
 
3.3%
4.93 31
 
3.1%
Other values (59) 390
39.0%
ValueCountFrequency (%)
-1 100
10.0%
2 1
 
0.1%
3 3
 
0.3%
3.5 2
 
0.2%
3.83 1
 
0.1%
ValueCountFrequency (%)
5 223
22.3%
4.99 10
 
1.0%
4.98 34
 
3.4%
4.97 48
 
4.8%
4.96 33
 
3.3%
Distinct65
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.27921
Minimum-1
Maximum5
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)10.0%
Memory size7.9 KiB
2023-10-23T21:04:25.962620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q14.76
median4.9
Q34.98
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation1.772025042
Coefficient of variation (CV)0.414100977
Kurtosis4.95065286
Mean4.27921
Median Absolute Deviation (MAD)0.1
Skewness-2.619053435
Sum4279.21
Variance3.140072749
MonotonicityNot monotonic
2023-10-23T21:04:26.669598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 220
22.0%
-1 100
 
10.0%
4.97 37
 
3.7%
4.88 36
 
3.6%
4.93 35
 
3.5%
4.95 34
 
3.4%
4.9 34
 
3.4%
4.96 32
 
3.2%
4.98 31
 
3.1%
4.92 30
 
3.0%
Other values (55) 411
41.1%
ValueCountFrequency (%)
-1 100
10.0%
1 1
 
0.1%
3.6 1
 
0.1%
4 8
 
0.8%
4.07 1
 
0.1%
ValueCountFrequency (%)
5 220
22.0%
4.99 14
 
1.4%
4.98 31
 
3.1%
4.97 37
 
3.7%
4.96 32
 
3.2%

review_scores_location
Real number (ℝ)

Distinct85
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.23365
Minimum-1
Maximum5
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)10.0%
Memory size7.9 KiB
2023-10-23T21:04:27.329223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q14.67
median4.88
Q34.97
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation1.762764339
Coefficient of variation (CV)0.4163698792
Kurtosis4.836753261
Mean4.23365
Median Absolute Deviation (MAD)0.115
Skewness-2.583798149
Sum4233.65
Variance3.107338116
MonotonicityNot monotonic
2023-10-23T21:04:28.059101image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 177
 
17.7%
-1 100
 
10.0%
4.93 41
 
4.1%
4.95 36
 
3.6%
4.94 34
 
3.4%
4.98 32
 
3.2%
4.97 31
 
3.1%
4.88 29
 
2.9%
4.92 27
 
2.7%
4.86 24
 
2.4%
Other values (75) 469
46.9%
ValueCountFrequency (%)
-1 100
10.0%
2 1
 
0.1%
3 2
 
0.2%
3.4 1
 
0.1%
3.5 2
 
0.2%
ValueCountFrequency (%)
5 177
17.7%
4.99 12
 
1.2%
4.98 32
 
3.2%
4.97 31
 
3.1%
4.96 20
 
2.0%

review_scores_value
Real number (ℝ)

Distinct95
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.06441
Minimum-1
Maximum5
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)10.0%
Memory size7.9 KiB
2023-10-23T21:04:28.736268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q14.43
median4.66
Q34.7925
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)0.3625

Descriptive statistics

Standard deviation1.71488556
Coefficient of variation (CV)0.4219273056
Kurtosis4.685302413
Mean4.06441
Median Absolute Deviation (MAD)0.16
Skewness-2.540859018
Sum4064.41
Variance2.940832484
MonotonicityNot monotonic
2023-10-23T21:04:29.323230image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 100
 
10.0%
5 77
 
7.7%
4 32
 
3.2%
4.67 30
 
3.0%
4.5 25
 
2.5%
4.71 24
 
2.4%
4.7 24
 
2.4%
4.77 23
 
2.3%
4.6 23
 
2.3%
4.83 22
 
2.2%
Other values (85) 620
62.0%
ValueCountFrequency (%)
-1 100
10.0%
1 1
 
0.1%
2.5 2
 
0.2%
3 3
 
0.3%
3.4 1
 
0.1%
ValueCountFrequency (%)
5 77
7.7%
4.96 3
 
0.3%
4.94 2
 
0.2%
4.93 3
 
0.3%
4.92 7
 
0.7%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
849 
True
151 
ValueCountFrequency (%)
False 849
84.9%
True 151
 
15.1%
2023-10-23T21:04:29.852186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct26
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.192
Minimum1
Maximum121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:04:30.317714image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile26
Maximum121
Range120
Interquartile range (IQR)3

Descriptive statistics

Standard deviation11.33197024
Coefficient of variation (CV)2.182582866
Kurtosis38.40475243
Mean5.192
Median Absolute Deviation (MAD)1
Skewness5.374694819
Sum5192
Variance128.4135495
MonotonicityNot monotonic
2023-10-23T21:04:31.069484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 448
44.8%
2 180
18.0%
3 85
 
8.5%
4 59
 
5.9%
5 39
 
3.9%
6 35
 
3.5%
8 23
 
2.3%
52 23
 
2.3%
10 22
 
2.2%
7 21
 
2.1%
Other values (16) 65
 
6.5%
ValueCountFrequency (%)
1 448
44.8%
2 180
18.0%
3 85
 
8.5%
4 59
 
5.9%
5 39
 
3.9%
ValueCountFrequency (%)
121 3
 
0.3%
52 23
2.3%
50 4
 
0.4%
34 3
 
0.3%
33 12
1.2%
Distinct26
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.324
Minimum0
Maximum121
Zeros169
Zeros (%)16.9%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:04:31.778623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile26
Maximum121
Range121
Interquartile range (IQR)2

Descriptive statistics

Standard deviation10.61299485
Coefficient of variation (CV)2.454439142
Kurtosis47.61427197
Mean4.324
Median Absolute Deviation (MAD)1
Skewness5.817660426
Sum4324
Variance112.6356597
MonotonicityNot monotonic
2023-10-23T21:04:32.453046image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 429
42.9%
0 169
 
16.9%
2 129
 
12.9%
3 54
 
5.4%
4 40
 
4.0%
6 25
 
2.5%
5 23
 
2.3%
7 23
 
2.3%
43 23
 
2.3%
10 16
 
1.6%
Other values (16) 69
 
6.9%
ValueCountFrequency (%)
0 169
 
16.9%
1 429
42.9%
2 129
 
12.9%
3 54
 
5.4%
4 40
 
4.0%
ValueCountFrequency (%)
121 3
 
0.3%
50 4
 
0.4%
43 23
2.3%
33 15
1.5%
32 1
 
0.1%
Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84
Minimum0
Maximum9
Zeros688
Zeros (%)68.8%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-23T21:04:33.095113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.793213767
Coefficient of variation (CV)2.134778295
Kurtosis9.54281414
Mean0.84
Median Absolute Deviation (MAD)0
Skewness2.985919531
Sum840
Variance3.215615616
MonotonicityNot monotonic
2023-10-23T21:04:33.681974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 688
68.8%
1 131
 
13.1%
2 67
 
6.7%
3 46
 
4.6%
9 25
 
2.5%
5 24
 
2.4%
4 13
 
1.3%
6 4
 
0.4%
8 2
 
0.2%
ValueCountFrequency (%)
0 688
68.8%
1 131
 
13.1%
2 67
 
6.7%
3 46
 
4.6%
4 13
 
1.3%
ValueCountFrequency (%)
9 25
2.5%
8 2
 
0.2%
6 4
 
0.4%
5 24
2.4%
4 13
1.3%

reviews_per_month
Real number (ℝ)

Distinct225
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46837
Minimum-1
Maximum4.59
Zeros0
Zeros (%)0.0%
Negative97
Negative (%)9.7%
Memory size7.9 KiB
2023-10-23T21:04:35.180686image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q10.06
median0.265
Q30.81
95-th percentile2.0915
Maximum4.59
Range5.59
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.8542715159
Coefficient of variation (CV)1.823924495
Kurtosis2.642020712
Mean0.46837
Median Absolute Deviation (MAD)0.245
Skewness1.040380003
Sum468.37
Variance0.7297798229
MonotonicityNot monotonic
2023-10-23T21:04:35.959950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 97
 
9.7%
0.04 35
 
3.5%
0.02 34
 
3.4%
0.01 27
 
2.7%
0.09 24
 
2.4%
0.03 24
 
2.4%
0.06 23
 
2.3%
0.1 21
 
2.1%
0.05 21
 
2.1%
0.08 20
 
2.0%
Other values (215) 674
67.4%
ValueCountFrequency (%)
-1 97
9.7%
0.01 27
 
2.7%
0.02 34
 
3.4%
0.03 24
 
2.4%
0.04 35
 
3.5%
ValueCountFrequency (%)
4.59 1
0.1%
4.58 1
0.1%
4.25 1
0.1%
4.07 1
0.1%
3.79 1
0.1%